---
_id: '998'
abstract:
- lang: eng
  text: 'A major open problem on the road to artificial intelligence is the development
    of incrementally learning systems that learn about more and more concepts over
    time from a stream of data. In this work, we introduce a new training strategy,
    iCaRL, that allows learning in such a class-incremental way: only the training
    data for a small number of classes has to be present at the same time and new
    classes can be added progressively. iCaRL learns strong classifiers and a data
    representation simultaneously. This distinguishes it from earlier works that were
    fundamentally limited to fixed data representations and therefore incompatible
    with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet
    ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period
    of time where other strategies quickly fail. '
article_processing_charge: No
arxiv: 1
author:
- first_name: Sylvestre Alvise
  full_name: Rebuffi, Sylvestre Alvise
  last_name: Rebuffi
- first_name: Alexander
  full_name: Kolesnikov, Alexander
  id: 2D157DB6-F248-11E8-B48F-1D18A9856A87
  last_name: Kolesnikov
- first_name: Georg
  full_name: Sperl, Georg
  id: 4DD40360-F248-11E8-B48F-1D18A9856A87
  last_name: Sperl
- first_name: Christoph
  full_name: Lampert, Christoph
  id: 40C20FD2-F248-11E8-B48F-1D18A9856A87
  last_name: Lampert
  orcid: 0000-0001-8622-7887
citation:
  ama: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier
    and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:<a href="https://doi.org/10.1109/CVPR.2017.587">10.1109/CVPR.2017.587</a>'
  apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., &#38; Lampert, C. (2017). iCaRL:
    Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542).
    Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA,
    United States: IEEE. <a href="https://doi.org/10.1109/CVPR.2017.587">https://doi.org/10.1109/CVPR.2017.587</a>'
  chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph
    Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42.
    IEEE, 2017. <a href="https://doi.org/10.1109/CVPR.2017.587">https://doi.org/10.1109/CVPR.2017.587</a>.'
  ieee: 'S. A. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert, “iCaRL: Incremental
    classifier and representation learning,” presented at the CVPR: Computer Vision
    and Pattern Recognition, Honolulu, HA, United States, 2017, vol. 2017, pp. 5533–5542.'
  ista: 'Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. 2017. iCaRL: Incremental classifier
    and representation learning. CVPR: Computer Vision and Pattern Recognition vol.
    2017, 5533–5542.'
  mla: 'Rebuffi, Sylvestre Alvise, et al. <i>ICaRL: Incremental Classifier and Representation
    Learning</i>. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:<a href="https://doi.org/10.1109/CVPR.2017.587">10.1109/CVPR.2017.587</a>.'
  short: S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
conference:
  end_date: 2017-07-26
  location: Honolulu, HA, United States
  name: 'CVPR: Computer Vision and Pattern Recognition'
  start_date: 2017-07-21
date_created: 2018-12-11T11:49:37Z
date_published: 2017-04-14T00:00:00Z
date_updated: 2025-06-04T08:18:32Z
day: '14'
department:
- _id: ChLa
- _id: ChWo
doi: 10.1109/CVPR.2017.587
ec_funded: 1
external_id:
  arxiv:
  - '1611.07725'
  isi:
  - '000418371405066'
intvolume: '      2017'
isi: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/1611.07725
month: '04'
oa: 1
oa_version: Submitted Version
page: 5533 - 5542
project:
- _id: 2532554C-B435-11E9-9278-68D0E5697425
  call_identifier: FP7
  grant_number: '308036'
  name: Lifelong Learning of Visual Scene Understanding
publication_identifier:
  isbn:
  - 978-153860457-1
publication_status: published
publisher: IEEE
publist_id: '6400'
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'iCaRL: Incremental classifier and representation learning'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 2017
year: '2017'
...