---
OA_place: publisher
_id: '8390'
abstract:
- lang: eng
text: "Deep neural networks have established a new standard for data-dependent feature
extraction pipelines in the Computer Vision literature. Despite their remarkable
performance in the standard supervised learning scenario, i.e. when models are
trained with labeled data and tested on samples that follow a similar distribution,
neural networks have been shown to struggle with more advanced generalization
abilities, such as transferring knowledge across visually different domains, or
generalizing to new unseen combinations of known concepts. In this thesis we argue
that, in contrast to the usual black-box behavior of neural networks, leveraging
more structured internal representations is a promising direction\r\nfor tackling
such problems. In particular, we focus on two forms of structure. First, we tackle
modularity: We show that (i) compositional architectures are a natural tool for
modeling reasoning tasks, in that they efficiently capture their combinatorial
nature, which is key for generalizing beyond the compositions seen during training.
We investigate how to to learn such models, both formally and experimentally,
for the task of abstract visual reasoning. Then, we show that (ii) in some settings,
modularity allows us to efficiently break down complex tasks into smaller, easier,
modules, thereby improving computational efficiency; We study this behavior in
the context of generative models for colorization, as well as for small objects
detection. Secondly, we investigate the inherently layered structure of representations
learned by neural networks, and analyze its role in the context of transfer learning
and domain adaptation across visually\r\ndissimilar domains. "
acknowledged_ssus:
- _id: CampIT
- _id: ScienComp
acknowledgement: Last but not least, I would like to acknowledge the support of the
IST IT and scientific computing team for helping provide a great work environment.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
citation:
ama: Royer A. Leveraging structure in Computer Vision tasks for flexible Deep Learning
models. 2020. doi:10.15479/AT:ISTA:8390
apa: Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:8390
chicago: Royer, Amélie. “Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models.” Institute of Science and Technology Austria, 2020. https://doi.org/10.15479/AT:ISTA:8390.
ieee: A. Royer, “Leveraging structure in Computer Vision tasks for flexible Deep
Learning models,” Institute of Science and Technology Austria, 2020.
ista: Royer A. 2020. Leveraging structure in Computer Vision tasks for flexible
Deep Learning models. Institute of Science and Technology Austria.
mla: Royer, Amélie. Leveraging Structure in Computer Vision Tasks for Flexible
Deep Learning Models. Institute of Science and Technology Austria, 2020, doi:10.15479/AT:ISTA:8390.
short: A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep
Learning Models, Institute of Science and Technology Austria, 2020.
corr_author: '1'
date_created: 2020-09-14T13:42:09Z
date_published: 2020-09-14T00:00:00Z
date_updated: 2026-04-08T07:26:44Z
day: '14'
ddc:
- '000'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:8390
file:
- access_level: open_access
checksum: c914d2f88846032f3d8507734861b6ee
content_type: application/pdf
creator: dernst
date_created: 2020-09-14T13:39:14Z
date_updated: 2020-09-14T13:39:14Z
file_id: '8391'
file_name: 2020_Thesis_Royer.pdf
file_size: 30224591
relation: main_file
success: 1
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checksum: ae98fb35d912cff84a89035ae5794d3c
content_type: application/x-zip-compressed
creator: dernst
date_created: 2020-09-14T13:39:17Z
date_updated: 2020-09-14T13:39:17Z
file_id: '8392'
file_name: thesis_sources.zip
file_size: 74227627
relation: main_file
file_date_updated: 2020-09-14T13:39:17Z
has_accepted_license: '1'
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-sa/4.0/
month: '09'
oa: 1
oa_version: Published Version
page: '197'
publication_identifier:
isbn:
- 978-3-99078-007-7
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '7936'
relation: part_of_dissertation
status: public
- id: '8092'
relation: part_of_dissertation
status: public
- id: '911'
relation: part_of_dissertation
status: public
- id: '8193'
relation: part_of_dissertation
status: public
- id: '7937'
relation: part_of_dissertation
status: public
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Leveraging structure in Computer Vision tasks for flexible Deep Learning models
tmp:
image: /images/cc_by_nc_sa.png
legal_code_url: https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC
BY-NC-SA 4.0)
short: CC BY-NC-SA (4.0)
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2020'
...
---
_id: '7937'
abstract:
- lang: eng
text: 'Fine-tuning is a popular way of exploiting knowledge contained in a pre-trained
convolutional network for a new visual recognition task. However, the orthogonal
setting of transferring knowledge from a pretrained network to a visually different
yet semantically close source is rarely considered: This commonly happens with
real-life data, which is not necessarily as clean as the training source (noise,
geometric transformations, different modalities, etc.).To tackle such scenarios,
we introduce a new, generalized form of fine-tuning, called flex-tuning, in which
any individual unit (e.g. layer) of a network can be tuned, and the most promising
one is chosen automatically. In order to make the method appealing for practical
use, we propose two lightweight and faster selection procedures that prove to
be good approximations in practice. We study these selection criteria empirically
across a variety of domain shifts and data scarcity scenarios, and show that fine-tuning
individual units, despite its simplicity, yields very good results as an adaptation
technique. As it turns out, in contrast to common practice, rather than the last
fully-connected unit it is best to tune an intermediate or early one in many domain-
shift scenarios, which is accurately detected by flex-tuning.'
article_number: 2180-2189
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. A flexible selection scheme for minimum-effort transfer
learning. In: 2020 IEEE Winter Conference on Applications of Computer Vision.
IEEE; 2020. doi:10.1109/WACV45572.2020.9093635'
apa: 'Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort
transfer learning. In 2020 IEEE Winter Conference on Applications of Computer
Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635'
chicago: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for
Minimum-Effort Transfer Learning.” In 2020 IEEE Winter Conference on Applications
of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093635.
ieee: A. Royer and C. Lampert, “A flexible selection scheme for minimum-effort transfer
learning,” in 2020 IEEE Winter Conference on Applications of Computer Vision,
Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. A flexible selection scheme for minimum-effort
transfer learning. 2020 IEEE Winter Conference on Applications of Computer Vision.
WACV: Winter Conference on Applications of Computer Vision, 2180–2189.'
mla: Royer, Amélie, and Christoph Lampert. “A Flexible Selection Scheme for Minimum-Effort
Transfer Learning.” 2020 IEEE Winter Conference on Applications of Computer
Vision, 2180–2189, IEEE, 2020, doi:10.1109/WACV45572.2020.9093635.
short: A. Royer, C. Lampert, in:, 2020 IEEE Winter Conference on Applications of
Computer Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: Snowmass Village, CO, United States
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2026-04-08T07:26:44Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093635
external_id:
arxiv:
- '2008.11995'
isi:
- '000578444802027'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://arxiv.org/abs/2008.11995
month: '03'
oa: 1
oa_version: Preprint
publication: 2020 IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: A flexible selection scheme for minimum-effort transfer learning
type: conference
user_id: 317138e5-6ab7-11ef-aa6d-ffef3953e345
year: '2020'
...
---
_id: '7936'
abstract:
- lang: eng
text: 'State-of-the-art detection systems are generally evaluated on their ability
to exhaustively retrieve objects densely distributed in the image, across a wide
variety of appearances and semantic categories. Orthogonal to this, many real-life
object detection applications, for example in remote sensing, instead require
dealing with large images that contain only a few small objects of a single class,
scattered heterogeneously across the space. In addition, they are often subject
to strict computational constraints, such as limited battery capacity and computing
power.To tackle these more practical scenarios, we propose a novel flexible detection
scheme that efficiently adapts to variable object sizes and densities: We rely
on a sequence of detection stages, each of which has the ability to predict groups
of objects as well as individuals. Similar to a detection cascade, this multi-stage
architecture spares computational effort by discarding large irrelevant regions
of the image early during the detection process. The ability to group objects
provides further computational and memory savings, as it allows working with lower
image resolutions in early stages, where groups are more easily detected than
individuals, as they are more salient. We report experimental results on two aerial
image datasets, and show that the proposed method is as accurate yet computationally
more efficient than standard single-shot detectors, consistently across three
different backbone architectures.'
article_number: 1716-1725
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Royer A, Lampert C. Localizing grouped instances for efficient detection in
low-resource scenarios. In: IEEE Winter Conference on Applications of Computer
Vision. IEEE; 2020. doi:10.1109/WACV45572.2020.9093288'
apa: 'Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient
detection in low-resource scenarios. In IEEE Winter Conference on Applications
of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288'
chicago: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for
Efficient Detection in Low-Resource Scenarios.” In IEEE Winter Conference on
Applications of Computer Vision. IEEE, 2020. https://doi.org/10.1109/WACV45572.2020.9093288.
ieee: A. Royer and C. Lampert, “Localizing grouped instances for efficient detection
in low-resource scenarios,” in IEEE Winter Conference on Applications of Computer
Vision, Snowmass Village, CO, United States, 2020.
ista: 'Royer A, Lampert C. 2020. Localizing grouped instances for efficient detection
in low-resource scenarios. IEEE Winter Conference on Applications of Computer
Vision. WACV: Winter Conference on Applications of Computer Vision, 1716–1725.'
mla: Royer, Amélie, and Christoph Lampert. “Localizing Grouped Instances for Efficient
Detection in Low-Resource Scenarios.” IEEE Winter Conference on Applications
of Computer Vision, 1716–1725, IEEE, 2020, doi:10.1109/WACV45572.2020.9093288.
short: A. Royer, C. Lampert, in:, IEEE Winter Conference on Applications of Computer
Vision, IEEE, 2020.
conference:
end_date: 2020-03-05
location: ' Snowmass Village, CO, United States'
name: 'WACV: Winter Conference on Applications of Computer Vision'
start_date: 2020-03-01
date_created: 2020-06-07T22:00:53Z
date_published: 2020-03-01T00:00:00Z
date_updated: 2026-04-08T07:26:43Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/WACV45572.2020.9093288
external_id:
arxiv:
- '2004.12623'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2004.12623
month: '03'
oa: 1
oa_version: Preprint
publication: IEEE Winter Conference on Applications of Computer Vision
publication_identifier:
isbn:
- '9781728165530'
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: 1
status: public
title: Localizing grouped instances for efficient detection in low-resource scenarios
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '8092'
abstract:
- lang: eng
text: Image translation refers to the task of mapping images from a visual domain
to another. Given two unpaired collections of images, we aim to learn a mapping
between the corpus-level style of each collection, while preserving semantic content
shared across the two domains. We introduce xgan, a dual adversarial auto-encoder,
which captures a shared representation of the common domain semantic content in
an unsupervised way, while jointly learning the domain-to-domain image translations
in both directions. We exploit ideas from the domain adaptation literature and
define a semantic consistency loss which encourages the learned embedding to preserve
semantics shared across domains. We report promising qualitative results for the
task of face-to-cartoon translation. The cartoon dataset we collected for this
purpose, “CartoonSet”, is also publicly available as a new benchmark for semantic
style transfer at https://google.github.io/cartoonset/index.html.
article_processing_charge: No
arxiv: 1
author:
- first_name: Amélie
full_name: Royer, Amélie
id: 3811D890-F248-11E8-B48F-1D18A9856A87
last_name: Royer
orcid: 0000-0002-8407-0705
- first_name: Konstantinos
full_name: Bousmalis, Konstantinos
last_name: Bousmalis
- first_name: Stephan
full_name: Gouws, Stephan
last_name: Gouws
- first_name: Fred
full_name: Bertsch, Fred
last_name: Bertsch
- first_name: Inbar
full_name: Mosseri, Inbar
last_name: Mosseri
- first_name: Forrester
full_name: Cole, Forrester
last_name: Cole
- first_name: Kevin
full_name: Murphy, Kevin
last_name: Murphy
citation:
ama: 'Royer A, Bousmalis K, Gouws S, et al. XGAN: Unsupervised image-to-image translation
for many-to-many mappings. In: Singh R, Vatsa M, Patel VM, Ratha N, eds. Domain
Adaptation for Visual Understanding. Springer Nature; 2020:33-49. doi:10.1007/978-3-030-30671-7_3'
apa: 'Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Mosseri, I., Cole, F., &
Murphy, K. (2020). XGAN: Unsupervised image-to-image translation for many-to-many
mappings. In R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain
Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3'
chicago: 'Royer, Amélie, Konstantinos Bousmalis, Stephan Gouws, Fred Bertsch, Inbar
Mosseri, Forrester Cole, and Kevin Murphy. “XGAN: Unsupervised Image-to-Image
Translation for Many-to-Many Mappings.” In Domain Adaptation for Visual Understanding,
edited by Richa Singh, Mayank Vatsa, Vishal M. Patel, and Nalini Ratha, 33–49.
Springer Nature, 2020. https://doi.org/10.1007/978-3-030-30671-7_3.'
ieee: 'A. Royer et al., “XGAN: Unsupervised image-to-image translation for
many-to-many mappings,” in Domain Adaptation for Visual Understanding,
R. Singh, M. Vatsa, V. M. Patel, and N. Ratha, Eds. Springer Nature, 2020, pp.
33–49.'
ista: 'Royer A, Bousmalis K, Gouws S, Bertsch F, Mosseri I, Cole F, Murphy K. 2020.XGAN:
Unsupervised image-to-image translation for many-to-many mappings. In: Domain
Adaptation for Visual Understanding. , 33–49.'
mla: 'Royer, Amélie, et al. “XGAN: Unsupervised Image-to-Image Translation for Many-to-Many
Mappings.” Domain Adaptation for Visual Understanding, edited by Richa
Singh et al., Springer Nature, 2020, pp. 33–49, doi:10.1007/978-3-030-30671-7_3.'
short: A. Royer, K. Bousmalis, S. Gouws, F. Bertsch, I. Mosseri, F. Cole, K. Murphy,
in:, R. Singh, M. Vatsa, V.M. Patel, N. Ratha (Eds.), Domain Adaptation for Visual
Understanding, Springer Nature, 2020, pp. 33–49.
date_created: 2020-07-05T22:00:46Z
date_published: 2020-01-08T00:00:00Z
date_updated: 2026-04-08T07:26:44Z
day: '08'
department:
- _id: ChLa
doi: 10.1007/978-3-030-30671-7_3
editor:
- first_name: Richa
full_name: Singh, Richa
last_name: Singh
- first_name: Mayank
full_name: Vatsa, Mayank
last_name: Vatsa
- first_name: Vishal M.
full_name: Patel, Vishal M.
last_name: Patel
- first_name: Nalini
full_name: Ratha, Nalini
last_name: Ratha
external_id:
arxiv:
- '1711.05139'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1711.05139
month: '01'
oa: 1
oa_version: Preprint
page: 33-49
publication: Domain Adaptation for Visual Understanding
publication_identifier:
isbn:
- '9783030306717'
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '8331'
relation: dissertation_contains
status: deleted
- id: '8390'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: 'XGAN: Unsupervised image-to-image translation for many-to-many mappings'
type: book_chapter
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '6482'
abstract:
- lang: eng
text: 'Computer vision systems for automatic image categorization have become accurate
and reliable enough that they can run continuously for days or even years as components
of real-world commercial applications. A major open problem in this context, however,
is quality control. Good classification performance can only be expected if systems
run under the specific conditions, in particular data distributions, that they
were trained for. Surprisingly, none of the currently used deep network architectures
have a built-in functionality that could detect if a network operates on data
from a distribution it was not trained for, such that potentially a warning to
the human users could be triggered. In this work, we describe KS(conf), a procedure
for detecting such outside of specifications (out-of-specs) operation, based on
statistical testing of the network outputs. We show by extensive experiments using
the ImageNet, AwA2 and DAVIS datasets on a variety of ConvNets architectures that
KS(conf) reliably detects out-of-specs situations. It furthermore has a number
of properties that make it a promising candidate for practical deployment: it
is easy to implement, adds almost no overhead to the system, works with all networks,
including pretrained ones, and requires no a priori knowledge of how the data
distribution could change. '
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Rémy
full_name: Sun, Rémy
last_name: Sun
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Sun R, Lampert C. KS(conf): A light-weight test if a ConvNet operates outside
of Its specifications. In: Vol 11269. Springer Nature; 2019:244-259. doi:10.1007/978-3-030-12939-2_18'
apa: 'Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet
operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at
the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature.
https://doi.org/10.1007/978-3-030-12939-2_18'
chicago: 'Sun, Rémy, and Christoph Lampert. “KS(Conf): A Light-Weight Test If a
ConvNet Operates Outside of Its Specifications,” 11269:244–59. Springer Nature,
2019. https://doi.org/10.1007/978-3-030-12939-2_18.'
ieee: 'R. Sun and C. Lampert, “KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications,” presented at the GCPR: Conference on Pattern Recognition,
Stuttgart, Germany, 2019, vol. 11269, pp. 244–259.'
ista: 'Sun R, Lampert C. 2019. KS(conf): A light-weight test if a ConvNet operates
outside of Its specifications. GCPR: Conference on Pattern Recognition, LNCS,
vol. 11269, 244–259.'
mla: 'Sun, Rémy, and Christoph Lampert. KS(Conf): A Light-Weight Test If a ConvNet
Operates Outside of Its Specifications. Vol. 11269, Springer Nature, 2019,
pp. 244–59, doi:10.1007/978-3-030-12939-2_18.'
short: R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.
conference:
end_date: 2018-10-12
location: Stuttgart, Germany
name: 'GCPR: Conference on Pattern Recognition'
start_date: 2018-10-09
date_created: 2019-05-24T09:48:36Z
date_published: 2019-02-14T00:00:00Z
date_updated: 2025-04-15T07:10:25Z
day: '14'
department:
- _id: ChLa
doi: 10.1007/978-3-030-12939-2_18
ec_funded: 1
external_id:
arxiv:
- '1804.04171'
intvolume: ' 11269'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1804.04171
month: '02'
oa: 1
oa_version: Preprint
page: 244-259
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
eissn:
- 1611-3349
isbn:
- '9783030129385'
- '9783030129392'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
record:
- id: '6944'
relation: later_version
status: public
scopus_import: '1'
status: public
title: 'KS(conf): A light-weight test if a ConvNet operates outside of Its specifications'
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 11269
year: '2019'
...
---
_id: '6554'
abstract:
- lang: eng
text: Due to the importance of zero-shot learning, i.e. classifying images where
there is a lack of labeled training data, the number of proposed approaches has
recently increased steadily. We argue that it is time to take a step back and
to analyze the status quo of the area. The purpose of this paper is three-fold.
First, given the fact that there is no agreed upon zero-shot learning benchmark,
we first define a new benchmark by unifying both the evaluation protocols and
data splits of publicly available datasets used for this task. This is an important
contribution as published results are often not comparable and sometimes even
flawed due to, e.g. pre-training on zero-shot test classes. Moreover, we propose
a new zero-shot learning dataset, the Animals with Attributes 2 (AWA2) dataset
which we make publicly available both in terms of image features and the images
themselves. Second, we compare and analyze a significant number of the state-of-the-art
methods in depth, both in the classic zero-shot setting but also in the more realistic
generalized zero-shot setting. Finally, we discuss in detail the limitations of
the current status of the area which can be taken as a basis for advancing it.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Yongqin
full_name: Xian, Yongqin
last_name: Xian
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0002-4561-241X
- first_name: Bernt
full_name: Schiele, Bernt
last_name: Schiele
- first_name: Zeynep
full_name: Akata, Zeynep
last_name: Akata
citation:
ama: Xian Y, Lampert C, Schiele B, Akata Z. Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern
Analysis and Machine Intelligence. 2019;41(9):2251-2265. doi:10.1109/tpami.2018.2857768
apa: Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning
- A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions
on Pattern Analysis and Machine Intelligence. Institute of Electrical and
Electronics Engineers. https://doi.org/10.1109/tpami.2018.2857768
chicago: Xian, Yongqin, Christoph Lampert, Bernt Schiele, and Zeynep Akata. “Zero-Shot
Learning - A Comprehensive Evaluation of the Good, the Bad and the Ugly.” IEEE
Transactions on Pattern Analysis and Machine Intelligence. Institute of Electrical
and Electronics Engineers, 2019. https://doi.org/10.1109/tpami.2018.2857768.
ieee: Y. Xian, C. Lampert, B. Schiele, and Z. Akata, “Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, vol. 41, no. 9. Institute of Electrical
and Electronics Engineers, pp. 2251–2265, 2019.
ista: Xian Y, Lampert C, Schiele B, Akata Z. 2019. Zero-shot learning - A comprehensive
evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis
and Machine Intelligence. 41(9), 2251–2265.
mla: Xian, Yongqin, et al. “Zero-Shot Learning - A Comprehensive Evaluation of the
Good, the Bad and the Ugly.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 41, no. 9, Institute of Electrical and Electronics Engineers,
2019, pp. 2251–65, doi:10.1109/tpami.2018.2857768.
short: Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis
and Machine Intelligence 41 (2019) 2251–2265.
date_created: 2019-06-11T14:05:59Z
date_published: 2019-09-01T00:00:00Z
date_updated: 2024-12-11T11:49:58Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/tpami.2018.2857768
external_id:
arxiv:
- '1707.00600'
isi:
- '000480343900015'
intvolume: ' 41'
isi: 1
issue: '9'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1707.00600
month: '09'
oa: 1
oa_version: Preprint
page: 2251 - 2265
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
eissn:
- 1939-3539
issn:
- 0162-8828
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: Zero-shot learning - A comprehensive evaluation of the good, the bad and the
ugly
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 41
year: '2019'
...
---
OA_place: publisher
OA_type: gold
_id: '6569'
abstract:
- lang: eng
text: 'Knowledge distillation, i.e. one classifier being trained on the outputs
of another classifier, is an empirically very successful technique for knowledge
transfer between classifiers. It has even been observed that classifiers learn
much faster and more reliably if trained with the outputs of another classifier
as soft labels, instead of from ground truth data. So far, however, there is no
satisfactory theoretical explanation of this phenomenon. In this work, we provide
the first insights into the working mechanisms of distillation by studying the
special case of linear and deep linear classifiers. Specifically, we prove a
generalization bound that establishes fast convergence of the expected risk of
a distillation-trained linear classifier. From the bound and its proof we extract
three keyfactors that determine the success of distillation: data geometry – geometric
properties of the datadistribution, in particular class separation, has an immediate
influence on the convergence speed of the risk; optimization bias– gradient descentoptimization
finds a very favorable minimum of the distillation objective; and strong monotonicity–
the expected risk of the student classifier always decreases when the size of
the training set grows.'
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Towards understanding knowledge distillation. In: Proceedings
of the 36th International Conference on Machine Learning. Vol 97. ML Research
Press; 2019:5142-5151.'
apa: 'Phuong, M., & Lampert, C. (2019). Towards understanding knowledge distillation.
In Proceedings of the 36th International Conference on Machine Learning
(Vol. 97, pp. 5142–5151). Long Beach, CA, United States: ML Research Press.'
chicago: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.”
In Proceedings of the 36th International Conference on Machine Learning,
97:5142–51. ML Research Press, 2019.
ieee: M. Phuong and C. Lampert, “Towards understanding knowledge distillation,”
in Proceedings of the 36th International Conference on Machine Learning,
Long Beach, CA, United States, 2019, vol. 97, pp. 5142–5151.
ista: 'Phuong M, Lampert C. 2019. Towards understanding knowledge distillation.
Proceedings of the 36th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 97, 5142–5151.'
mla: Phuong, Mary, and Christoph Lampert. “Towards Understanding Knowledge Distillation.”
Proceedings of the 36th International Conference on Machine Learning, vol.
97, ML Research Press, 2019, pp. 5142–51.
short: M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference
on Machine Learning, ML Research Press, 2019, pp. 5142–5151.
conference:
end_date: 2019-06-15
location: Long Beach, CA, United States
name: 'ICML: International Conference on Machine Learning'
start_date: 2019-06-10
corr_author: '1'
date_created: 2019-06-20T18:23:03Z
date_published: 2019-06-13T00:00:00Z
date_updated: 2025-05-20T07:48:49Z
day: '13'
ddc:
- '000'
department:
- _id: ChLa
file:
- access_level: open_access
checksum: a66d00e2694d749250f8507f301320ca
content_type: application/pdf
creator: bphuong
date_created: 2019-06-20T18:22:56Z
date_updated: 2020-07-14T12:47:33Z
file_id: '6570'
file_name: paper.pdf
file_size: 686432
relation: main_file
file_date_updated: 2020-07-14T12:47:33Z
has_accepted_license: '1'
intvolume: ' 97'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 5142-5151
publication: Proceedings of the 36th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Towards understanding knowledge distillation
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2019'
...
---
_id: '6942'
abstract:
- lang: eng
text: "Graph games and Markov decision processes (MDPs) are standard models in reactive
synthesis and verification of probabilistic systems with nondeterminism. The class
of \U0001D714 -regular winning conditions; e.g., safety, reachability, liveness,
parity conditions; provides a robust and expressive specification formalism for
properties that arise in analysis of reactive systems. The resolutions of nondeterminism
in games and MDPs are represented as strategies, and we consider succinct representation
of such strategies. The decision-tree data structure from machine learning retains
the flavor of decisions of strategies and allows entropy-based minimization to
obtain succinct trees. However, in contrast to traditional machine-learning problems
where small errors are allowed, for winning strategies in graph games and MDPs
no error is allowed, and the decision tree must represent the entire strategy.
In this work we propose decision trees with linear classifiers for representation
of strategies in graph games and MDPs. We have implemented strategy representation
using this data structure and we present experimental results for problems on
graph games and MDPs, which show that this new data structure presents a much
more efficient strategy representation as compared to standard decision trees."
alternative_title:
- LNCS
article_processing_charge: No
arxiv: 1
author:
- first_name: Pranav
full_name: Ashok, Pranav
last_name: Ashok
- first_name: Tomáš
full_name: Brázdil, Tomáš
last_name: Brázdil
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Jan
full_name: Křetínský, Jan
last_name: Křetínský
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Viktor
full_name: Toman, Viktor
id: 3AF3DA7C-F248-11E8-B48F-1D18A9856A87
last_name: Toman
orcid: 0000-0001-9036-063X
citation:
ama: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. Strategy
representation by decision trees with linear classifiers. In: 16th International
Conference on Quantitative Evaluation of Systems. Vol 11785. Springer Nature;
2019:109-128. doi:10.1007/978-3-030-30281-8_7'
apa: 'Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., &
Toman, V. (2019). Strategy representation by decision trees with linear classifiers.
In 16th International Conference on Quantitative Evaluation of Systems
(Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7'
chicago: Ashok, Pranav, Tomáš Brázdil, Krishnendu Chatterjee, Jan Křetínský, Christoph
Lampert, and Viktor Toman. “Strategy Representation by Decision Trees with Linear
Classifiers.” In 16th International Conference on Quantitative Evaluation of
Systems, 11785:109–28. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-30281-8_7.
ieee: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, and V. Toman,
“Strategy representation by decision trees with linear classifiers,” in 16th
International Conference on Quantitative Evaluation of Systems, Glasgow, United
Kingdom, 2019, vol. 11785, pp. 109–128.
ista: 'Ashok P, Brázdil T, Chatterjee K, Křetínský J, Lampert C, Toman V. 2019.
Strategy representation by decision trees with linear classifiers. 16th International
Conference on Quantitative Evaluation of Systems. QEST: Quantitative Evaluation
of Systems, LNCS, vol. 11785, 109–128.'
mla: Ashok, Pranav, et al. “Strategy Representation by Decision Trees with Linear
Classifiers.” 16th International Conference on Quantitative Evaluation of Systems,
vol. 11785, Springer Nature, 2019, pp. 109–28, doi:10.1007/978-3-030-30281-8_7.
short: P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, V. Toman,
in:, 16th International Conference on Quantitative Evaluation of Systems, Springer
Nature, 2019, pp. 109–128.
conference:
end_date: 2019-09-12
location: Glasgow, United Kingdom
name: 'QEST: Quantitative Evaluation of Systems'
start_date: 2019-09-10
date_created: 2019-10-14T06:57:49Z
date_published: 2019-09-04T00:00:00Z
date_updated: 2025-04-14T13:51:05Z
day: '04'
department:
- _id: KrCh
- _id: ChLa
doi: 10.1007/978-3-030-30281-8_7
external_id:
arxiv:
- '1906.08178'
isi:
- '000679281300007'
intvolume: ' 11785'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.08178
month: '09'
oa: 1
oa_version: Preprint
page: 109-128
project:
- _id: 25863FF4-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11407
name: Game Theory
- _id: 25F2ACDE-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: S11402-N23
name: Rigorous Systems Engineering
- _id: 25892FC0-B435-11E9-9278-68D0E5697425
grant_number: ICT15-003
name: Efficient Algorithms for Computer Aided Verification
publication: 16th International Conference on Quantitative Evaluation of Systems
publication_identifier:
eisbn:
- '9783030302818'
isbn:
- '9783030302801'
issn:
- 0302-9743
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Strategy representation by decision trees with linear classifiers
type: conference
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 11785
year: '2019'
...
---
_id: '7171'
abstract:
- lang: ger
text: "Wissen Sie, was sich hinter künstlicher Intelligenz und maschinellem Lernen
verbirgt? \r\nDieses Sachbuch erklärt Ihnen leicht verständlich und ohne komplizierte
Formeln die grundlegenden Methoden und Vorgehensweisen des maschinellen Lernens.
Mathematisches Vorwissen ist dafür nicht nötig. Kurzweilig und informativ illustriert
Lisa, die Protagonistin des Buches, diese anhand von Alltagssituationen. \r\nEin
Buch für alle, die in Diskussionen über Chancen und Risiken der aktuellen Entwicklung
der künstlichen Intelligenz und des maschinellen Lernens mit Faktenwissen punkten
möchten. Auch für Schülerinnen und Schüler geeignet!"
article_processing_charge: No
citation:
ama: 'Kersting K, Lampert C, Rothkopf C, eds. Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden: Springer Nature; 2019.
doi:10.1007/978-3-658-26763-6'
apa: 'Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen
Lernen: Künstliche Intelligenz Verständlich Erklärt (1st ed.). Wiesbaden:
Springer Nature. https://doi.org/10.1007/978-3-658-26763-6'
chicago: 'Kersting, Kristian, Christoph Lampert, and Constantin Rothkopf, eds. Wie
Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt. 1st ed. Wiesbaden:
Springer Nature, 2019. https://doi.org/10.1007/978-3-658-26763-6.'
ieee: 'K. Kersting, C. Lampert, and C. Rothkopf, Eds., Wie Maschinen Lernen:
Künstliche Intelligenz Verständlich Erklärt, 1st ed. Wiesbaden: Springer Nature,
2019.'
ista: 'Kersting K, Lampert C, Rothkopf C eds. 2019. Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt 1st ed., Wiesbaden: Springer Nature, XIV, 245p.'
mla: 'Kersting, Kristian, et al., editors. Wie Maschinen Lernen: Künstliche Intelligenz
Verständlich Erklärt. 1st ed., Springer Nature, 2019, doi:10.1007/978-3-658-26763-6.'
short: 'K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche
Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.'
date_created: 2019-12-11T14:15:56Z
date_published: 2019-10-30T00:00:00Z
date_updated: 2021-12-22T14:40:58Z
day: '30'
department:
- _id: ChLa
doi: 10.1007/978-3-658-26763-6
edition: '1'
editor:
- first_name: Kristian
full_name: Kersting, Kristian
last_name: Kersting
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Constantin
full_name: Rothkopf, Constantin
last_name: Rothkopf
language:
- iso: ger
month: '10'
oa_version: None
page: XIV, 245
place: Wiesbaden
publication_identifier:
eisbn:
- 978-3-658-26763-6
isbn:
- 978-3-658-26762-9
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- description: News on IST Website
relation: press_release
url: https://ist.ac.at/en/news/book-release-how-machines-learn/
status: public
title: 'Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt'
type: book_editor
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2019'
...
---
_id: '7640'
abstract:
- lang: eng
text: We propose a new model for detecting visual relationships, such as "person
riding motorcycle" or "bottle on table". This task is an important step towards
comprehensive structured mage understanding, going beyond detecting individual
objects. Our main novelty is a Box Attention mechanism that allows to model pairwise
interactions between objects using standard object detection pipelines. The resulting
model is conceptually clean, expressive and relies on well-justified training
and prediction procedures. Moreover, unlike previously proposed approaches, our
model does not introduce any additional complex components or hyperparameters
on top of those already required by the underlying detection model. We conduct
an experimental evaluation on two datasets, V-COCO and Open Images, demonstrating
strong quantitative and qualitative results.
article_number: 1749-1753
article_processing_charge: No
arxiv: 1
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
- first_name: Alina
full_name: Kuznetsova, Alina
last_name: Kuznetsova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. Detecting visual relationships
using box attention. In: Proceedings of the 2019 International Conference on
Computer Vision Workshop. IEEE; 2019. doi:10.1109/ICCVW.2019.00217'
apa: 'Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting
visual relationships using box attention. In Proceedings of the 2019 International
Conference on Computer Vision Workshop. Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217'
chicago: Kolesnikov, Alexander, Alina Kuznetsova, Christoph Lampert, and Vittorio
Ferrari. “Detecting Visual Relationships Using Box Attention.” In Proceedings
of the 2019 International Conference on Computer Vision Workshop. IEEE, 2019.
https://doi.org/10.1109/ICCVW.2019.00217.
ieee: A. Kolesnikov, A. Kuznetsova, C. Lampert, and V. Ferrari, “Detecting visual
relationships using box attention,” in Proceedings of the 2019 International
Conference on Computer Vision Workshop, Seoul, South Korea, 2019.
ista: 'Kolesnikov A, Kuznetsova A, Lampert C, Ferrari V. 2019. Detecting visual
relationships using box attention. Proceedings of the 2019 International Conference
on Computer Vision Workshop. ICCVW: International Conference on Computer Vision
Workshop, 1749–1753.'
mla: Kolesnikov, Alexander, et al. “Detecting Visual Relationships Using Box Attention.”
Proceedings of the 2019 International Conference on Computer Vision Workshop,
1749–1753, IEEE, 2019, doi:10.1109/ICCVW.2019.00217.
short: A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of
the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
conference:
end_date: 2019-10-28
location: Seoul, South Korea
name: 'ICCVW: International Conference on Computer Vision Workshop'
start_date: 2019-10-27
date_created: 2020-04-05T22:00:51Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2025-04-15T07:10:23Z
day: '01'
department:
- _id: ChLa
doi: 10.1109/ICCVW.2019.00217
ec_funded: 1
external_id:
arxiv:
- '1807.02136'
isi:
- '000554591601098'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1807.02136
month: '10'
oa: 1
oa_version: Preprint
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 2019 International Conference on Computer Vision Workshop
publication_identifier:
isbn:
- '9781728150239'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Detecting visual relationships using box attention
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2019'
...
---
_id: '6590'
abstract:
- lang: eng
text: 'Modern machine learning methods often require more data for training than
a single expert can provide. Therefore, it has become a standard procedure to
collect data from external sources, e.g. via crowdsourcing. Unfortunately, the
quality of these sources is not always guaranteed. As additional complications,
the data might be stored in a distributed way, or might even have to remain private.
In this work, we address the question of how to learn robustly in such scenarios.
Studying the problem through the lens of statistical learning theory, we derive
a procedure that allows for learning from all available sources, yet automatically
suppresses irrelevant or corrupted data. We show by extensive experiments that
our method provides significant improvements over alternative approaches from
robust statistics and distributed optimization. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
orcid: 0009-0009-5204-7621
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Konstantinov NH, Lampert C. Robust learning from untrusted sources. In: Proceedings
of the 36th International Conference on Machine Learning. Vol 97. ML Research
Press; 2019:3488-3498.'
apa: 'Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted
sources. In Proceedings of the 36th International Conference on Machine Learning
(Vol. 97, pp. 3488–3498). Long Beach, CA, USA: ML Research Press.'
chicago: Konstantinov, Nikola H, and Christoph Lampert. “Robust Learning from Untrusted
Sources.” In Proceedings of the 36th International Conference on Machine Learning,
97:3488–98. ML Research Press, 2019.
ieee: N. H. Konstantinov and C. Lampert, “Robust learning from untrusted sources,”
in Proceedings of the 36th International Conference on Machine Learning,
Long Beach, CA, USA, 2019, vol. 97, pp. 3488–3498.
ista: 'Konstantinov NH, Lampert C. 2019. Robust learning from untrusted sources.
Proceedings of the 36th International Conference on Machine Learning. ICML: International
Conference on Machine Learning vol. 97, 3488–3498.'
mla: Konstantinov, Nikola H., and Christoph Lampert. “Robust Learning from Untrusted
Sources.” Proceedings of the 36th International Conference on Machine Learning,
vol. 97, ML Research Press, 2019, pp. 3488–98.
short: N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International
Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.
conference:
end_date: 2919-06-15
location: Long Beach, CA, USA
name: 'ICML: International Conference on Machine Learning'
start_date: 2019-06-10
date_created: 2019-06-27T14:18:23Z
date_published: 2019-06-01T00:00:00Z
date_updated: 2026-04-07T14:19:48Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1901.10310'
intvolume: ' 97'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1901.10310
month: '06'
oa: 1
oa_version: Preprint
page: 3488-3498
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Proceedings of the 36th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
record:
- id: '10799'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Robust learning from untrusted sources
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 97
year: '2019'
...
---
_id: '7479'
abstract:
- lang: eng
text: "Multi-exit architectures, in which a stack of processing layers is interleaved
with early output layers, allow the processing of a test example to stop early
and thus save computation time and/or energy. In this work, we propose a new
training procedure for multi-exit architectures based on the principle of knowledge
distillation. The method encourage searly exits to mimic later, more accurate
exits, by matching their output probabilities.\r\nExperiments on CIFAR100 and
\ ImageNet show that distillation-based training significantly improves the
accuracy of early exits while maintaining state-of-the-art accuracy for late
\ ones. The method is particularly beneficial when training data is limited
\ and it allows a straightforward extension to semi-supervised learning,i.e.
making use of unlabeled data at training time. Moreover, it takes only afew lines
to implement and incurs almost no computational overhead at training time, and
none at all at test time."
article_processing_charge: No
author:
- first_name: Phuong
full_name: Bui Thi Mai, Phuong
id: 3EC6EE64-F248-11E8-B48F-1D18A9856A87
last_name: Bui Thi Mai
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Phuong M, Lampert C. Distillation-based training for multi-exit architectures.
In: IEEE International Conference on Computer Vision. Vol 2019-October.
IEEE; 2019:1355-1364. doi:10.1109/ICCV.2019.00144'
apa: 'Phuong, M., & Lampert, C. (2019). Distillation-based training for multi-exit
architectures. In IEEE International Conference on Computer Vision (Vol.
2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144'
chicago: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” In IEEE International Conference on Computer Vision, 2019–October:1355–64.
IEEE, 2019. https://doi.org/10.1109/ICCV.2019.00144.
ieee: M. Phuong and C. Lampert, “Distillation-based training for multi-exit architectures,”
in IEEE International Conference on Computer Vision, Seoul, Korea, 2019,
vol. 2019–October, pp. 1355–1364.
ista: 'Phuong M, Lampert C. 2019. Distillation-based training for multi-exit architectures.
IEEE International Conference on Computer Vision. ICCV: International Conference
on Computer Vision vol. 2019–October, 1355–1364.'
mla: Phuong, Mary, and Christoph Lampert. “Distillation-Based Training for Multi-Exit
Architectures.” IEEE International Conference on Computer Vision, vol.
2019–October, IEEE, 2019, pp. 1355–64, doi:10.1109/ICCV.2019.00144.
short: M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision,
IEEE, 2019, pp. 1355–1364.
conference:
end_date: 2019-11-02
location: Seoul, Korea
name: 'ICCV: International Conference on Computer Vision'
start_date: 2019-10-27
date_created: 2020-02-11T09:06:57Z
date_published: 2019-10-01T00:00:00Z
date_updated: 2026-04-08T07:01:16Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1109/ICCV.2019.00144
ec_funded: 1
external_id:
isi:
- '000531438101047'
file:
- access_level: open_access
checksum: 7b77fb5c2d27c4c37a7612ba46a66117
content_type: application/pdf
creator: bphuong
date_created: 2020-02-11T09:06:39Z
date_updated: 2020-07-14T12:47:59Z
file_id: '7480'
file_name: main.pdf
file_size: 735768
relation: main_file
file_date_updated: 2020-07-14T12:47:59Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
month: '10'
oa: 1
oa_version: Submitted Version
page: 1355-1364
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: IEEE International Conference on Computer Vision
publication_identifier:
isbn:
- '9781728148038'
issn:
- 1550-5499
publication_status: published
publisher: IEEE
quality_controlled: '1'
related_material:
record:
- id: '9418'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Distillation-based training for multi-exit architectures
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2019-October
year: '2019'
...
---
_id: '321'
abstract:
- lang: eng
text: The twelve papers in this special section focus on learning systems with shared
information for computer vision and multimedia communication analysis. In the
real world, a realistic setting for computer vision or multimedia recognition
problems is that we have some classes containing lots of training data and many
classes containing a small amount of training data. Therefore, how to use frequent
classes to help learning rare classes for which it is harder to collect the training
data is an open question. Learning with shared information is an emerging topic
in machine learning, computer vision and multimedia analysis. There are different
levels of components that can be shared during concept modeling and machine learning
stages, such as sharing generic object parts, sharing attributes, sharing transformations,
sharing regularization parameters and sharing training examples, etc. Regarding
the specific methods, multi-task learning, transfer learning and deep learning
can be seen as using different strategies to share information. These learning
with shared information methods are very effective in solving real-world large-scale
problems.
article_processing_charge: No
article_type: original
author:
- first_name: Trevor
full_name: Darrell, Trevor
last_name: Darrell
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Nico
full_name: Sebe, Nico
last_name: Sebe
- first_name: Ying
full_name: Wu, Ying
last_name: Wu
- first_name: Yan
full_name: Yan, Yan
last_name: Yan
citation:
ama: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. Guest editors’ introduction to the
special section on learning with Shared information for computer vision and multimedia
analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998
apa: Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). Guest editors’
introduction to the special section on learning with Shared information for computer
vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine
Intelligence. IEEE. https://doi.org/10.1109/TPAMI.2018.2804998
chicago: Darrell, Trevor, Christoph Lampert, Nico Sebe, Ying Wu, and Yan Yan. “Guest
Editors’ Introduction to the Special Section on Learning with Shared Information
for Computer Vision and Multimedia Analysis.” IEEE Transactions on Pattern
Analysis and Machine Intelligence. IEEE, 2018. https://doi.org/10.1109/TPAMI.2018.2804998.
ieee: T. Darrell, C. Lampert, N. Sebe, Y. Wu, and Y. Yan, “Guest editors’ introduction
to the special section on learning with Shared information for computer vision
and multimedia analysis,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 40, no. 5. IEEE, pp. 1029–1031, 2018.
ista: Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. 2018. Guest editors’ introduction
to the special section on learning with Shared information for computer vision
and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence.
40(5), 1029–1031.
mla: Darrell, Trevor, et al. “Guest Editors’ Introduction to the Special Section
on Learning with Shared Information for Computer Vision and Multimedia Analysis.”
IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40,
no. 5, IEEE, 2018, pp. 1029–31, doi:10.1109/TPAMI.2018.2804998.
short: T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern
Analysis and Machine Intelligence 40 (2018) 1029–1031.
corr_author: '1'
date_created: 2018-12-11T11:45:48Z
date_published: 2018-05-01T00:00:00Z
date_updated: 2024-10-09T20:58:26Z
day: '01'
ddc:
- '000'
department:
- _id: ChLa
doi: 10.1109/TPAMI.2018.2804998
external_id:
isi:
- '000428901200001'
file:
- access_level: open_access
checksum: b19c75da06faf3291a3ca47dfa50ef63
content_type: application/pdf
creator: dernst
date_created: 2020-05-14T12:50:48Z
date_updated: 2020-07-14T12:46:03Z
file_id: '7835'
file_name: 2018_IEEE_Darrell.pdf
file_size: 141724
relation: main_file
file_date_updated: 2020-07-14T12:46:03Z
has_accepted_license: '1'
intvolume: ' 40'
isi: 1
issue: '5'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: 1029 - 1031
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: published
publisher: IEEE
publist_id: '7544'
quality_controlled: '1'
scopus_import: '1'
status: public
title: Guest editors' introduction to the special section on learning with Shared
information for computer vision and multimedia analysis
type: journal_article
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
volume: 40
year: '2018'
...
---
_id: '10882'
abstract:
- lang: eng
text: 'We introduce Intelligent Annotation Dialogs for bounding box annotation.
We train an agent to automatically choose a sequence of actions for a human annotator
to produce a bounding box in a minimal amount of time. Specifically, we consider
two actions: box verification [34], where the annotator verifies a box generated
by an object detector, and manual box drawing. We explore two kinds of agents,
one based on predicting the probability that a box will be positively verified,
and the other based on reinforcement learning. We demonstrate that (1) our agents
are able to learn efficient annotation strategies in several scenarios, automatically
adapting to the image difficulty, the desired quality of the boxes, and the detector
strength; (2) in all scenarios the resulting annotation dialogs speed up annotation
compared to manual box drawing alone and box verification alone, while also outperforming
any fixed combination of verification and drawing in most scenarios; (3) in a
realistic scenario where the detector is iteratively re-trained, our agents evolve
a series of strategies that reflect the shifting trade-off between verification
and drawing as the detector grows stronger.'
article_processing_charge: No
arxiv: 1
author:
- first_name: Jasper
full_name: Uijlings, Jasper
last_name: Uijlings
- first_name: Ksenia
full_name: Konyushkova, Ksenia
last_name: Konyushkova
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Vittorio
full_name: Ferrari, Vittorio
last_name: Ferrari
citation:
ama: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs
for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956'
apa: 'Uijlings, J., Konyushkova, K., Lampert, C., & Ferrari, V. (2018). Learning
intelligent dialogs for bounding box annotation. In 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition (pp. 9175–9184). Salt Lake City,
UT, United States: IEEE. https://doi.org/10.1109/cvpr.2018.00956'
chicago: Uijlings, Jasper, Ksenia Konyushkova, Christoph Lampert, and Vittorio Ferrari.
“Learning Intelligent Dialogs for Bounding Box Annotation.” In 2018 IEEE/CVF
Conference on Computer Vision and Pattern Recognition, 9175–84. IEEE, 2018.
https://doi.org/10.1109/cvpr.2018.00956.
ieee: J. Uijlings, K. Konyushkova, C. Lampert, and V. Ferrari, “Learning intelligent
dialogs for bounding box annotation,” in 2018 IEEE/CVF Conference on Computer
Vision and Pattern Recognition, Salt Lake City, UT, United States, 2018, pp.
9175–9184.
ista: 'Uijlings J, Konyushkova K, Lampert C, Ferrari V. 2018. Learning intelligent
dialogs for bounding box annotation. 2018 IEEE/CVF Conference on Computer Vision
and Pattern Recognition. CVF: Conference on Computer Vision and Pattern Recognition,
9175–9184.'
mla: Uijlings, Jasper, et al. “Learning Intelligent Dialogs for Bounding Box Annotation.”
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE,
2018, pp. 9175–84, doi:10.1109/cvpr.2018.00956.
short: J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference
on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
conference:
end_date: 2018-06-23
location: Salt Lake City, UT, United States
name: 'CVF: Conference on Computer Vision and Pattern Recognition'
start_date: 2018-06-18
corr_author: '1'
date_created: 2022-03-18T12:45:09Z
date_published: 2018-12-17T00:00:00Z
date_updated: 2024-10-09T21:02:26Z
day: '17'
department:
- _id: ChLa
doi: 10.1109/cvpr.2018.00956
external_id:
arxiv:
- '1712.08087'
isi:
- '000457843609036'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: ' https://doi.org/10.48550/arXiv.1712.08087'
month: '12'
oa: 1
oa_version: Preprint
page: 9175-9184
publication: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
publication_identifier:
eissn:
- 2575-7075
isbn:
- '9781538664209'
publication_status: published
publisher: IEEE
quality_controlled: '1'
scopus_import: '1'
status: public
title: Learning intelligent dialogs for bounding box annotation
type: conference
user_id: c635000d-4b10-11ee-a964-aac5a93f6ac1
year: '2018'
...
---
_id: '5584'
abstract:
- lang: eng
text: "This package contains data for the publication \"Nonlinear decoding of a
complex movie from the mammalian retina\" by Deny S. et al, PLOS Comput Biol (2018).
\r\n\r\nThe data consists of\r\n(i) 91 spike sorted, isolated rat retinal ganglion
cells that pass stability and quality criteria, recorded on the multi-electrode
array, in response to the presentation of the complex movie with many randomly
moving dark discs. The responses are represented as 648000 x 91 binary matrix,
where the first index indicates the timebin of duration 12.5 ms, and the second
index the neural identity. The matrix entry is 0/1 if the neuron didn't/did spike
in the particular time bin.\r\n(ii) README file and a graphical illustration of
the structure of the experiment, specifying how the 648000 timebins are split
into epochs where 1, 2, 4, or 10 discs were displayed, and which stimulus segments
are exact repeats or unique ball trajectories.\r\n(iii) a 648000 x 400 matrix
of luminance traces for each of the 20 x 20 positions (\"sites\") in the movie
frame, with time that is locked to the recorded raster. The luminance traces are
produced as described in the manuscript by filtering the raw disc movie with a
small gaussian spatial kernel. "
article_processing_charge: No
author:
- first_name: Stephane
full_name: Deny, Stephane
last_name: Deny
- first_name: Olivier
full_name: Marre, Olivier
last_name: Marre
- first_name: Vicente
full_name: Botella-Soler, Vicente
last_name: Botella-Soler
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
- first_name: Gasper
full_name: Tkacik, Gasper
id: 3D494DCA-F248-11E8-B48F-1D18A9856A87
last_name: Tkacik
orcid: 0000-0002-6699-1455
citation:
ama: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. Nonlinear decoding
of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
apa: Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018).
Nonlinear decoding of a complex movie from the mammalian retina. Institute of
Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:98
chicago: Deny, Stephane, Olivier Marre, Vicente Botella-Soler, Georg S Martius,
and Gašper Tkačik. “Nonlinear Decoding of a Complex Movie from the Mammalian Retina.”
Institute of Science and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:98.
ieee: S. Deny, O. Marre, V. Botella-Soler, G. S. Martius, and G. Tkačik, “Nonlinear
decoding of a complex movie from the mammalian retina.” Institute of Science and
Technology Austria, 2018.
ista: Deny S, Marre O, Botella-Soler V, Martius GS, Tkačik G. 2018. Nonlinear decoding
of a complex movie from the mammalian retina, Institute of Science and Technology
Austria, 10.15479/AT:ISTA:98.
mla: Deny, Stephane, et al. Nonlinear Decoding of a Complex Movie from the Mammalian
Retina. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:98.
short: S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
datarep_id: '98'
date_created: 2018-12-12T12:31:39Z
date_published: 2018-03-29T00:00:00Z
date_updated: 2025-04-15T08:18:24Z
day: '29'
ddc:
- '570'
department:
- _id: ChLa
- _id: GaTk
doi: 10.15479/AT:ISTA:98
file:
- access_level: open_access
checksum: 6808748837b9afbbbabc2a356ca2b88a
content_type: application/octet-stream
creator: system
date_created: 2018-12-12T13:02:24Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5590'
file_name: IST-2018-98-v1+1_BBalls_area2_tile2_20x20.mat
file_size: 1142543971
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content_type: application/pdf
creator: system
date_created: 2018-12-12T13:02:25Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5591'
file_name: IST-2018-98-v1+2_ExperimentStructure.pdf
file_size: 702336
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checksum: 0c9cfb4dab35bb3dc25a04395600b1c8
content_type: application/octet-stream
creator: system
date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5592'
file_name: IST-2018-98-v1+3_GoodLocations_area2_20x20.mat
file_size: 432
relation: main_file
- access_level: open_access
checksum: 2a83b011012e21e934b4596285b1a183
content_type: text/plain
creator: system
date_created: 2018-12-12T13:02:26Z
date_updated: 2020-07-14T12:47:07Z
file_id: '5593'
file_name: IST-2018-98-v1+4_README.txt
file_size: 986
relation: main_file
file_date_updated: 2020-07-14T12:47:07Z
has_accepted_license: '1'
keyword:
- retina
- decoding
- regression
- neural networks
- complex stimulus
license: https://creativecommons.org/publicdomain/zero/1.0/
month: '03'
oa: 1
oa_version: Published Version
project:
- _id: 254D1A94-B435-11E9-9278-68D0E5697425
call_identifier: FWF
grant_number: P 25651-N26
name: Sensitivity to higher-order statistics in natural scenes
publisher: Institute of Science and Technology Austria
related_material:
record:
- id: '292'
relation: used_in_publication
status: public
status: public
title: Nonlinear decoding of a complex movie from the mammalian retina
tmp:
image: /images/cc_0.png
legal_code_url: https://creativecommons.org/publicdomain/zero/1.0/legalcode
name: Creative Commons Public Domain Dedication (CC0 1.0)
short: CC0 (1.0)
type: research_data
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2018'
...
---
_id: '6011'
abstract:
- lang: eng
text: 'We establish a data-dependent notion of algorithmic stability for Stochastic
Gradient Descent (SGD), and employ it to develop novel generalization bounds.
This is in contrast to previous distribution-free algorithmic stability results
for SGD which depend on the worst-case constants. By virtue of the data-dependent
argument, our bounds provide new insights into learning with SGD on convex and
non-convex problems. In the convex case, we show that the bound on the generalization
error depends on the risk at the initialization point. In the non-convex case,
we prove that the expected curvature of the objective function around the initialization
point has crucial influence on the generalization error. In both cases, our results
suggest a simple data-driven strategy to stabilize SGD by pre-screening its initialization.
As a corollary, our results allow us to show optimistic generalization bounds
that exhibit fast convergence rates for SGD subject to a vanishing empirical risk
and low noise of stochastic gradient. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Ilja
full_name: Kuzborskij, Ilja
last_name: Kuzborskij
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
citation:
ama: 'Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent.
In: Proceedings of the 35 Th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:2815-2824.'
apa: 'Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic
gradient descent. In Proceedings of the 35 th International Conference on Machine
Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: ML Research Press.'
chicago: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” In Proceedings of the 35 Th International Conference on
Machine Learning, 80:2815–24. ML Research Press, 2018.
ieee: I. Kuzborskij and C. Lampert, “Data-dependent stability of stochastic gradient
descent,” in Proceedings of the 35 th International Conference on Machine Learning,
Stockholm, Sweden, 2018, vol. 80, pp. 2815–2824.
ista: 'Kuzborskij I, Lampert C. 2018. Data-dependent stability of stochastic gradient
descent. Proceedings of the 35 th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 2815–2824.'
mla: Kuzborskij, Ilja, and Christoph Lampert. “Data-Dependent Stability of Stochastic
Gradient Descent.” Proceedings of the 35 Th International Conference on Machine
Learning, vol. 80, ML Research Press, 2018, pp. 2815–24.
short: I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference
on Machine Learning, ML Research Press, 2018, pp. 2815–2824.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T14:51:57Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2025-04-15T07:10:23Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1703.01678'
isi:
- '000683379202095'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1703.01678
month: '02'
oa: 1
oa_version: Preprint
page: 2815-2824
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication: Proceedings of the 35 th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
scopus_import: '1'
status: public
title: Data-dependent stability of stochastic gradient descent
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '6012'
abstract:
- lang: eng
text: We present an approach to identify concise equations from data using a shallow
neural network approach. In contrast to ordinary black-box regression, this approach
allows understanding functional relations and generalizing them from observed
data to unseen parts of the parameter space. We show how to extend the class of
learnable equations for a recently proposed equation learning network to include
divisions, and we improve the learning and model selection strategy to be useful
for challenging real-world data. For systems governed by analytical expressions,
our method can in many cases identify the true underlying equation and extrapolate
to unseen domains. We demonstrate its effectiveness by experiments on a cart-pendulum
system, where only 2 random rollouts are required to learn the forward dynamics
and successfully achieve the swing-up task.
article_processing_charge: No
arxiv: 1
author:
- first_name: Subham
full_name: Sahoo, Subham
last_name: Sahoo
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
- first_name: Georg S
full_name: Martius, Georg S
id: 3A276B68-F248-11E8-B48F-1D18A9856A87
last_name: Martius
citation:
ama: 'Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control.
In: Proceedings of the 35th International Conference on Machine Learning.
Vol 80. ML Research Press; 2018:4442-4450.'
apa: 'Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for
extrapolation and control. In Proceedings of the 35th International Conference
on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: ML Research
Press.'
chicago: Sahoo, Subham, Christoph Lampert, and Georg S Martius. “Learning Equations
for Extrapolation and Control.” In Proceedings of the 35th International Conference
on Machine Learning, 80:4442–50. ML Research Press, 2018.
ieee: S. Sahoo, C. Lampert, and G. S. Martius, “Learning equations for extrapolation
and control,” in Proceedings of the 35th International Conference on Machine
Learning, Stockholm, Sweden, 2018, vol. 80, pp. 4442–4450.
ista: 'Sahoo S, Lampert C, Martius GS. 2018. Learning equations for extrapolation
and control. Proceedings of the 35th International Conference on Machine Learning.
ICML: International Conference on Machine Learning vol. 80, 4442–4450.'
mla: Sahoo, Subham, et al. “Learning Equations for Extrapolation and Control.” Proceedings
of the 35th International Conference on Machine Learning, vol. 80, ML Research
Press, 2018, pp. 4442–50.
short: S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International
Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: 'ICML: International Conference on Machine Learning'
start_date: 2018-07-10
date_created: 2019-02-14T15:21:07Z
date_published: 2018-02-01T00:00:00Z
date_updated: 2025-04-15T06:50:24Z
day: '01'
department:
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1806.07259'
isi:
- '000683379204058'
intvolume: ' 80'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1806.07259
month: '02'
oa: 1
oa_version: Preprint
page: 4442-4450
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
publication: Proceedings of the 35th International Conference on Machine Learning
publication_status: published
publisher: ML Research Press
quality_controlled: '1'
related_material:
link:
- description: News on IST Homepage
relation: press_release
url: https://ist.ac.at/en/news/first-machine-learning-method-capable-of-accurate-extrapolation/
scopus_import: '1'
status: public
title: Learning equations for extrapolation and control
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 80
year: '2018'
...
---
_id: '6589'
abstract:
- lang: eng
text: Distributed training of massive machine learning models, in particular deep
neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace.
Several families of communication-reduction methods, such as quantization, large-batch
methods, and gradient sparsification, have been proposed. To date, gradient sparsification
methods--where each node sorts gradients by magnitude, and only communicates a
subset of the components, accumulating the rest locally--are known to yield some
of the largest practical gains. Such methods can reduce the amount of communication
per step by up to \emph{three orders of magnitude}, while preserving model accuracy.
Yet, this family of methods currently has no theoretical justification. This is
the question we address in this paper. We prove that, under analytic assumptions,
sparsifying gradients by magnitude with local error correction provides convergence
guarantees, for both convex and non-convex smooth objectives, for data-parallel
SGD. The main insight is that sparsification methods implicitly maintain bounds
on the maximum impact of stale updates, thanks to selection by magnitude. Our
analysis and empirical validation also reveal that these methods do require analytical
conditions to converge well, justifying existing heuristics.
article_processing_charge: No
arxiv: 1
author:
- first_name: Dan-Adrian
full_name: Alistarh, Dan-Adrian
id: 4A899BFC-F248-11E8-B48F-1D18A9856A87
last_name: Alistarh
orcid: 0000-0003-3650-940X
- first_name: Torsten
full_name: Hoefler, Torsten
last_name: Hoefler
- first_name: Mikael
full_name: Johansson, Mikael
last_name: Johansson
- first_name: Nikola H
full_name: Konstantinov, Nikola H
id: 4B9D76E4-F248-11E8-B48F-1D18A9856A87
last_name: Konstantinov
- first_name: Sarit
full_name: Khirirat, Sarit
last_name: Khirirat
- first_name: Cedric
full_name: Renggli, Cedric
last_name: Renggli
citation:
ama: 'Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli
C. The convergence of sparsified gradient methods. In: Advances in Neural Information
Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems
Foundation; 2018:5973-5983.'
apa: 'Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat,
S., & Renggli, C. (2018). The convergence of sparsified gradient methods.
In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018,
pp. 5973–5983). Montreal, Canada: Neural Information Processing Systems Foundation.'
chicago: Alistarh, Dan-Adrian, Torsten Hoefler, Mikael Johansson, Nikola H Konstantinov,
Sarit Khirirat, and Cedric Renggli. “The Convergence of Sparsified Gradient Methods.”
In Advances in Neural Information Processing Systems 31, Volume 2018:5973–83.
Neural Information Processing Systems Foundation, 2018.
ieee: D.-A. Alistarh, T. Hoefler, M. Johansson, N. H. Konstantinov, S. Khirirat,
and C. Renggli, “The convergence of sparsified gradient methods,” in Advances
in Neural Information Processing Systems 31, Montreal, Canada, 2018, vol.
Volume 2018, pp. 5973–5983.
ista: 'Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli
C. 2018. The convergence of sparsified gradient methods. Advances in Neural Information
Processing Systems 31. NeurIPS: Conference on Neural Information Processing Systems
vol. Volume 2018, 5973–5983.'
mla: Alistarh, Dan-Adrian, et al. “The Convergence of Sparsified Gradient Methods.”
Advances in Neural Information Processing Systems 31, vol. Volume 2018,
Neural Information Processing Systems Foundation, 2018, pp. 5973–83.
short: D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat,
C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural
Information Processing Systems Foundation, 2018, pp. 5973–5983.
conference:
end_date: 2018-12-08
location: Montreal, Canada
name: 'NeurIPS: Conference on Neural Information Processing Systems'
start_date: 2018-12-02
corr_author: '1'
date_created: 2019-06-27T09:32:55Z
date_published: 2018-12-01T00:00:00Z
date_updated: 2025-06-26T12:23:06Z
day: '01'
department:
- _id: DaAl
- _id: ChLa
ec_funded: 1
external_id:
arxiv:
- '1809.10505'
isi:
- '000461852000047'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1809.10505
month: '12'
oa: 1
oa_version: Preprint
page: 5973-5983
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '665385'
name: International IST Doctoral Program
publication: Advances in Neural Information Processing Systems 31
publication_status: published
publisher: Neural Information Processing Systems Foundation
quality_controlled: '1'
scopus_import: '1'
status: public
title: The convergence of sparsified gradient methods
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: Volume 2018
year: '2018'
...
---
OA_place: publisher
_id: '197'
abstract:
- lang: eng
text: Modern computer vision systems heavily rely on statistical machine learning
models, which typically require large amounts of labeled data to be learned reliably.
Moreover, very recently computer vision research widely adopted techniques for
representation learning, which further increase the demand for labeled data. However,
for many important practical problems there is relatively small amount of labeled
data available, so it is problematic to leverage full potential of the representation
learning methods. One way to overcome this obstacle is to invest substantial resources
into producing large labelled datasets. Unfortunately, this can be prohibitively
expensive in practice. In this thesis we focus on the alternative way of tackling
the aforementioned issue. We concentrate on methods, which make use of weakly-labeled
or even unlabeled data. Specifically, the first half of the thesis is dedicated
to the semantic image segmentation task. We develop a technique, which achieves
competitive segmentation performance and only requires annotations in a form of
global image-level labels instead of dense segmentation masks. Subsequently, we
present a new methodology, which further improves segmentation performance by
leveraging tiny additional feedback from a human annotator. By using our methods
practitioners can greatly reduce the amount of data annotation effort, which is
required to learn modern image segmentation models. In the second half of the
thesis we focus on methods for learning from unlabeled visual data. We study a
family of autoregressive models for modeling structure of natural images and discuss
potential applications of these models. Moreover, we conduct in-depth study of
one of these applications, where we develop the state-of-the-art model for the
probabilistic image colorization task.
acknowledgement: I also gratefully acknowledge the support of NVIDIA Corporation with
the donation of the GPUs used for this research.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Kolesnikov, Alexander
id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
last_name: Kolesnikov
citation:
ama: Kolesnikov A. Weakly-Supervised Segmentation and Unsupervised Modeling of Natural
Images. 2018. doi:10.15479/AT:ISTA:th_1021
apa: Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria. https://doi.org/10.15479/AT:ISTA:th_1021
chicago: Kolesnikov, Alexander. “Weakly-Supervised Segmentation and Unsupervised
Modeling of Natural Images.” Institute of Science and Technology Austria, 2018.
https://doi.org/10.15479/AT:ISTA:th_1021.
ieee: A. Kolesnikov, “Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images,” Institute of Science and Technology Austria, 2018.
ista: Kolesnikov A. 2018. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria.
mla: Kolesnikov, Alexander. Weakly-Supervised Segmentation and Unsupervised Modeling
of Natural Images. Institute of Science and Technology Austria, 2018, doi:10.15479/AT:ISTA:th_1021.
short: A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of
Natural Images, Institute of Science and Technology Austria, 2018.
corr_author: '1'
date_created: 2018-12-11T11:45:09Z
date_published: 2018-05-25T00:00:00Z
date_updated: 2026-04-08T14:05:16Z
day: '25'
ddc:
- '004'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:th_1021
ec_funded: 1
file:
- access_level: open_access
checksum: bc678e02468d8ebc39dc7267dfb0a1c4
content_type: application/pdf
creator: system
date_created: 2018-12-12T10:14:57Z
date_updated: 2020-07-14T12:45:22Z
file_id: '5113'
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file_size: 12918758
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checksum: bc66973b086da5a043f1162dcfb1fde4
content_type: application/zip
creator: dernst
date_created: 2019-04-05T09:34:49Z
date_updated: 2020-07-14T12:45:22Z
file_id: '6225'
file_name: 2018_Thesis_Kolesnikov_source.zip
file_size: 55973760
relation: source_file
file_date_updated: 2020-07-14T12:45:22Z
has_accepted_license: '1'
language:
- iso: eng
month: '05'
oa: 1
oa_version: Published Version
page: '113'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7718'
pubrep_id: '1021'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2018'
...
---
OA_place: publisher
_id: '68'
abstract:
- lang: eng
text: The most common assumption made in statistical learning theory is the assumption
of the independent and identically distributed (i.i.d.) data. While being very
convenient mathematically, it is often very clearly violated in practice. This
disparity between the machine learning theory and applications underlies a growing
demand in the development of algorithms that learn from dependent data and theory
that can provide generalization guarantees similar to the independent situations.
This thesis is dedicated to two variants of dependencies that can arise in practice.
One is a dependence on the level of samples in a single learning task. Another
dependency type arises in the multi-task setting when the tasks are dependent
on each other even though the data for them can be i.i.d. In both cases we model
the data (samples or tasks) as stochastic processes and introduce new algorithms
for both settings that take into account and exploit the resulting dependencies.
We prove the theoretical guarantees on the performance of the introduced algorithms
under different evaluation criteria and, in addition, we compliment the theoretical
study by the empirical one, where we evaluate some of the algorithms on two real
world datasets to highlight their practical applicability.
alternative_title:
- ISTA Thesis
article_processing_charge: No
author:
- first_name: Alexander
full_name: Zimin, Alexander
id: 37099E9C-F248-11E8-B48F-1D18A9856A87
last_name: Zimin
citation:
ama: Zimin A. Learning from dependent data. 2018. doi:10.15479/AT:ISTA:TH1048
apa: Zimin, A. (2018). Learning from dependent data. Institute of Science
and Technology Austria. https://doi.org/10.15479/AT:ISTA:TH1048
chicago: Zimin, Alexander. “Learning from Dependent Data.” Institute of Science
and Technology Austria, 2018. https://doi.org/10.15479/AT:ISTA:TH1048.
ieee: A. Zimin, “Learning from dependent data,” Institute of Science and Technology
Austria, 2018.
ista: Zimin A. 2018. Learning from dependent data. Institute of Science and Technology
Austria.
mla: Zimin, Alexander. Learning from Dependent Data. Institute of Science
and Technology Austria, 2018, doi:10.15479/AT:ISTA:TH1048.
short: A. Zimin, Learning from Dependent Data, Institute of Science and Technology
Austria, 2018.
corr_author: '1'
date_created: 2018-12-11T11:44:27Z
date_published: 2018-09-01T00:00:00Z
date_updated: 2026-04-08T14:05:50Z
day: '01'
ddc:
- '004'
- '519'
degree_awarded: PhD
department:
- _id: ChLa
doi: 10.15479/AT:ISTA:TH1048
ec_funded: 1
file:
- access_level: open_access
checksum: e849dd40a915e4d6c5572b51b517f098
content_type: application/pdf
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6253'
file_name: 2018_Thesis_Zimin.pdf
file_size: 1036137
relation: main_file
- access_level: closed
checksum: da092153cec55c97461bd53c45c5d139
content_type: application/zip
creator: dernst
date_created: 2019-04-09T07:32:47Z
date_updated: 2020-07-14T12:47:40Z
file_id: '6254'
file_name: 2018_Thesis_Zimin_Source.zip
file_size: 637490
relation: source_file
file_date_updated: 2020-07-14T12:47:40Z
has_accepted_license: '1'
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
page: '92'
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '308036'
name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
issn:
- 2663-337X
publication_status: published
publisher: Institute of Science and Technology Austria
publist_id: '7986'
pubrep_id: '1048'
status: public
supervisor:
- first_name: Christoph
full_name: Lampert, Christoph
id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
last_name: Lampert
orcid: 0000-0001-8622-7887
title: Learning from dependent data
type: dissertation
user_id: ba8df636-2132-11f1-aed0-ed93e2281fdd
year: '2018'
...