--- _id: '11736' abstract: - lang: eng text: "This paper introduces a methodology for inverse-modeling of yarn-level mechanics of cloth, based on the mechanical response of fabrics in the real world. We compiled a database from physical tests of several different knitted fabrics used in the textile industry. These data span different types of complex knit patterns, yarn compositions, and fabric finishes, and the results demonstrate diverse physical properties like stiffness, nonlinearity, and anisotropy.\r\n\r\nWe then develop a system for approximating these mechanical responses with yarn-level cloth simulation. To do so, we introduce an efficient pipeline for converting between fabric-level data and yarn-level simulation, including a novel swatch-level approximation for speeding up computation, and some small-but-necessary extensions to yarn-level models used in computer graphics. The dataset used for this paper can be found at http://mslab.es/projects/YarnLevelFabrics." acknowledged_ssus: - _id: ScienComp acknowledgement: We wish to thank the anonymous reviewers for their helpful comments. To develop this project, we were helped by many people both at Under Armour (Clay Dean, Randall Harward, Kyle Blakely, Craig Simile, Michael Seiz, Brooke Malone, Brittainy McFarland, Emilie Phan, Lindsey Kern, Courtney Oswald, Haley Barkley, Bob Chin, Adam Bayer, Connie Kwok, Marielle Newman, Nick Pence, Allison Hicks, Allison White, Candace Rubenstein, Jeremy Stangland, Fred Fagergren, Michael Mazzoleni, Nathaniel Berry, Manuel Frank) and SEDDI (Gabriel Cirio, Alejandro Rodríguez, Sofía Dominguez, Alicia Nicas, Elena Garcés, Daniel Rodríguez, David Pascual, Manuel Godoy, Sergio Suja, Sergio Ruiz, Roberto Condori, Alberto Martín, Graham Sullivan). We also thank the members of the Visual Computing Group at IST Austria and the Multimodal Simulation Lab at URJC for their feedback. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing, and it was funded in part by the European Research Council (ERC Consolidator Grant 772738 TouchDesign). article_number: '65' article_processing_charge: No article_type: original author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl - first_name: Rosa M. full_name: Sánchez-Banderas, Rosa M. last_name: Sánchez-Banderas - first_name: Manwen full_name: Li, Manwen last_name: Li - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 - first_name: Miguel A. full_name: Otaduy, Miguel A. last_name: Otaduy citation: ama: Sperl G, Sánchez-Banderas RM, Li M, Wojtan C, Otaduy MA. Estimation of yarn-level simulation models for production fabrics. ACM Transactions on Graphics. 2022;41(4). doi:10.1145/3528223.3530167 apa: Sperl, G., Sánchez-Banderas, R. M., Li, M., Wojtan, C., & Otaduy, M. A. (2022). Estimation of yarn-level simulation models for production fabrics. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3528223.3530167 chicago: Sperl, Georg, Rosa M. Sánchez-Banderas, Manwen Li, Chris Wojtan, and Miguel A. Otaduy. “Estimation of Yarn-Level Simulation Models for Production Fabrics.” ACM Transactions on Graphics. Association for Computing Machinery, 2022. https://doi.org/10.1145/3528223.3530167. ieee: G. Sperl, R. M. Sánchez-Banderas, M. Li, C. Wojtan, and M. A. Otaduy, “Estimation of yarn-level simulation models for production fabrics,” ACM Transactions on Graphics, vol. 41, no. 4. Association for Computing Machinery, 2022. ista: Sperl G, Sánchez-Banderas RM, Li M, Wojtan C, Otaduy MA. 2022. Estimation of yarn-level simulation models for production fabrics. ACM Transactions on Graphics. 41(4), 65. mla: Sperl, Georg, et al. “Estimation of Yarn-Level Simulation Models for Production Fabrics.” ACM Transactions on Graphics, vol. 41, no. 4, 65, Association for Computing Machinery, 2022, doi:10.1145/3528223.3530167. short: G. Sperl, R.M. Sánchez-Banderas, M. Li, C. Wojtan, M.A. Otaduy, ACM Transactions on Graphics 41 (2022). date_created: 2022-08-07T22:01:58Z date_published: 2022-07-22T00:00:00Z date_updated: 2023-08-03T12:38:30Z day: '22' department: - _id: ChWo doi: 10.1145/3528223.3530167 external_id: isi: - '000830989200114' intvolume: ' 41' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1145/3528223.3530167 month: '07' oa: 1 oa_version: Published Version publication: ACM Transactions on Graphics publication_identifier: eissn: - 1557-7368 issn: - 0730-0301 publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: link: - description: News on the ISTA website relation: press_release url: https://ista.ac.at/en/news/digital-yarn-real-socks/ record: - id: '12358' relation: dissertation_contains status: public scopus_import: '1' status: public title: Estimation of yarn-level simulation models for production fabrics type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 41 year: '2022' ... --- _id: '12358' abstract: - lang: eng text: "The complex yarn structure of knitted and woven fabrics gives rise to both a mechanical and\r\nvisual complexity. The small-scale interactions of yarns colliding with and pulling on each\r\nother result in drastically different large-scale stretching and bending behavior, introducing\r\nanisotropy, curling, and more. While simulating cloth as individual yarns can reproduce this\r\ncomplexity and match the quality of real fabric, it may be too computationally expensive for\r\nlarge fabrics. On the other hand, continuum-based approaches do not need to discretize the\r\ncloth at a stitch-level, but it is non-trivial to find a material model that would replicate the\r\nlarge-scale behavior of yarn fabrics, and they discard the intricate visual detail. In this thesis,\r\nwe discuss three methods to try and bridge the gap between small-scale and large-scale yarn\r\nmechanics using numerical homogenization: fitting a continuum model to periodic yarn simulations, adding mechanics-aware yarn detail onto thin-shell simulations, and quantitatively\r\nfitting yarn parameters to physical measurements of real fabric.\r\nTo start, we present a method for animating yarn-level cloth effects using a thin-shell solver.\r\nWe first use a large number of periodic yarn-level simulations to build a model of the potential\r\nenergy density of the cloth, and then use it to compute forces in a thin-shell simulator. The\r\nresulting simulations faithfully reproduce expected effects like the stiffening of woven fabrics\r\nand the highly deformable nature and anisotropy of knitted fabrics at a fraction of the cost of\r\nfull yarn-level simulation.\r\nWhile our thin-shell simulations are able to capture large-scale yarn mechanics, they lack\r\nthe rich visual detail of yarn-level simulations. Therefore, we propose a method to animate\r\nyarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware\r\nfashion in real time. Using triangle strains to interpolate precomputed yarn geometry, we are\r\nable to reproduce effects such as knit loops tightening under stretching at negligible cost.\r\nFinally, we introduce a methodology for inverse-modeling of yarn-level mechanics of cloth,\r\nbased on the mechanical response of fabrics in the real world. We compile a database from\r\nphysical tests of several knitted fabrics used in the textile industry spanning diverse physical\r\nproperties like stiffness, nonlinearity, and anisotropy. We then develop a system for approximating these mechanical responses with yarn-level cloth simulation, using homogenized\r\nshell models to speed up computation and adding some small-but-necessary extensions to\r\nyarn-level models used in computer graphics.\r\n" acknowledged_ssus: - _id: SSU alternative_title: - ISTA Thesis article_processing_charge: No author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl citation: ama: 'Sperl G. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. 2022. doi:10.15479/at:ista:12103' apa: 'Sperl, G. (2022). Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria. https://doi.org/10.15479/at:ista:12103' chicago: 'Sperl, Georg. “Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting.” Institute of Science and Technology Austria, 2022. https://doi.org/10.15479/at:ista:12103.' ieee: 'G. Sperl, “Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting,” Institute of Science and Technology Austria, 2022.' ista: 'Sperl G. 2022. Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting. Institute of Science and Technology Austria.' mla: 'Sperl, Georg. Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting. Institute of Science and Technology Austria, 2022, doi:10.15479/at:ista:12103.' short: 'G. Sperl, Homogenizing Yarn Simulations: Large-Scale Mechanics, Small-Scale Detail, and Quantitative Fitting, Institute of Science and Technology Austria, 2022.' date_created: 2023-01-24T10:49:46Z date_published: 2022-09-22T00:00:00Z date_updated: 2024-02-28T12:57:46Z day: '22' ddc: - '000' - '620' degree_awarded: PhD department: - _id: GradSch - _id: ChWo doi: 10.15479/at:ista:12103 ec_funded: 1 file: - access_level: open_access checksum: 083722acbb8115e52e3b0fdec6226769 content_type: application/pdf creator: cchlebak date_created: 2023-01-25T12:04:41Z date_updated: 2023-02-02T09:29:57Z description: 'This is the main PDF file of the thesis. File size: 105 MB' file_id: '12371' file_name: thesis_gsperl.pdf file_size: 104497530 relation: main_file title: Thesis - access_level: open_access checksum: 511f82025e5fcb70bff4731d6896ca07 content_type: application/pdf creator: cchlebak date_created: 2023-02-02T09:33:37Z date_updated: 2023-02-02T09:33:37Z description: This version of the thesis uses stronger image compression for a smaller file size of 23MB. file_id: '12483' file_name: thesis_gsperl_compressed.pdf file_size: 23183710 relation: main_file title: Thesis (compressed 23MB) - access_level: open_access checksum: ed4cb85225eedff761c25bddfc37a2ed content_type: application/x-zip-compressed creator: cchlebak date_created: 2023-02-02T09:39:25Z date_updated: 2023-02-02T09:39:25Z file_id: '12484' file_name: thesis-source.zip file_size: 98382247 relation: source_file file_date_updated: 2023-02-02T09:39:25Z has_accepted_license: '1' language: - iso: eng month: '09' oa: 1 oa_version: Published Version page: '138' project: - _id: 2533E772-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '638176' name: Efficient Simulation of Natural Phenomena at Extremely Large Scales publication_identifier: isbn: - 978-3-99078-020-6 issn: - 2663-337X publication_status: published publisher: Institute of Science and Technology Austria related_material: record: - id: '11736' relation: part_of_dissertation status: public - id: '9818' relation: part_of_dissertation status: public - id: '8385' relation: part_of_dissertation status: public status: public supervisor: - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 title: 'Homogenizing yarn simulations: Large-scale mechanics, small-scale detail, and quantitative fitting' type: dissertation user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2022' ... --- _id: '9818' abstract: - lang: eng text: Triangle mesh-based simulations are able to produce satisfying animations of knitted and woven cloth; however, they lack the rich geometric detail of yarn-level simulations. Naive texturing approaches do not consider yarn-level physics, while full yarn-level simulations may become prohibitively expensive for large garments. We propose a method to animate yarn-level cloth geometry on top of an underlying deforming mesh in a mechanics-aware fashion. Using triangle strains to interpolate precomputed yarn geometry, we are able to reproduce effects such as knit loops tightening under stretching. In combination with precomputed mesh animation or real-time mesh simulation, our method is able to animate yarn-level cloth in real-time at large scales. acknowledged_ssus: - _id: ScienComp acknowledgement: "We wish to thank the anonymous reviewers and the members of the Visual Computing Group at IST Austria for their valuable feedback. We also thank Seddi Labs for providing the garment model with fold-over seams.\r\nThis research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific\r\nComputing. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 638176. Rahul Narain is supported by a Pankaj Gupta Young Faculty Fellowship and a gift from Adobe Inc." article_number: '168' article_processing_charge: Yes (in subscription journal) article_type: original author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl - first_name: Rahul full_name: Narain, Rahul last_name: Narain - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: Sperl G, Narain R, Wojtan C. Mechanics-aware deformation of yarn pattern geometry. ACM Transactions on Graphics. 2021;40(4). doi:10.1145/3450626.3459816 apa: Sperl, G., Narain, R., & Wojtan, C. (2021). Mechanics-aware deformation of yarn pattern geometry. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3450626.3459816 chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Mechanics-Aware Deformation of Yarn Pattern Geometry.” ACM Transactions on Graphics. Association for Computing Machinery, 2021. https://doi.org/10.1145/3450626.3459816. ieee: G. Sperl, R. Narain, and C. Wojtan, “Mechanics-aware deformation of yarn pattern geometry,” ACM Transactions on Graphics, vol. 40, no. 4. Association for Computing Machinery, 2021. ista: Sperl G, Narain R, Wojtan C. 2021. Mechanics-aware deformation of yarn pattern geometry. ACM Transactions on Graphics. 40(4), 168. mla: Sperl, Georg, et al. “Mechanics-Aware Deformation of Yarn Pattern Geometry.” ACM Transactions on Graphics, vol. 40, no. 4, 168, Association for Computing Machinery, 2021, doi:10.1145/3450626.3459816. short: G. Sperl, R. Narain, C. Wojtan, ACM Transactions on Graphics 40 (2021). date_created: 2021-08-08T22:01:27Z date_published: 2021-08-01T00:00:00Z date_updated: 2023-08-10T14:24:36Z day: '01' department: - _id: GradSch - _id: ChWo doi: 10.1145/3450626.3459816 ec_funded: 1 external_id: isi: - '000674930900132' intvolume: ' 40' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1145/3450626.3459816 month: '08' oa: 1 oa_version: Published Version project: - _id: 2533E772-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '638176' name: Efficient Simulation of Natural Phenomena at Extremely Large Scales publication: ACM Transactions on Graphics publication_identifier: eissn: - '15577368' issn: - '07300301' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: link: - description: News on IST Webpage relation: press_release url: https://ist.ac.at/en/news/knitting-virtual-yarn/ record: - id: '12358' relation: dissertation_contains status: public - id: '9327' relation: software status: public scopus_import: '1' status: public title: Mechanics-aware deformation of yarn pattern geometry type: journal_article user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 volume: 40 year: '2021' ... --- _id: '9327' abstract: - lang: eng text: "This archive contains the missing sweater mesh animations and displacement models for the code of \"Mechanics-Aware Deformation of Yarn Pattern Geometry\"\r\n\r\nCode Repository: https://git.ist.ac.at/gsperl/MADYPG" author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl - first_name: Rahul full_name: Narain, Rahul last_name: Narain - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: Sperl G, Narain R, Wojtan C. Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data). 2021. doi:10.15479/AT:ISTA:9327 apa: Sperl, G., Narain, R., & Wojtan, C. (2021). Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data). IST Austria. https://doi.org/10.15479/AT:ISTA:9327 chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data).” IST Austria, 2021. https://doi.org/10.15479/AT:ISTA:9327. ieee: G. Sperl, R. Narain, and C. Wojtan, “Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data).” IST Austria, 2021. ista: Sperl G, Narain R, Wojtan C. 2021. Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data), IST Austria, 10.15479/AT:ISTA:9327. mla: Sperl, Georg, et al. Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data). IST Austria, 2021, doi:10.15479/AT:ISTA:9327. short: G. Sperl, R. Narain, C. Wojtan, (2021). date_created: 2021-04-16T14:26:19Z date_published: 2021-05-01T00:00:00Z date_updated: 2023-08-10T14:24:36Z ddc: - '005' department: - _id: GradSch - _id: ChWo doi: 10.15479/AT:ISTA:9327 file: - access_level: open_access checksum: 0324cb519273371708743f3282e7c081 content_type: application/zip creator: gsperl date_created: 2021-04-16T14:15:12Z date_updated: 2021-04-16T14:15:12Z file_id: '9328' file_name: MADYPG_extra_data.zip file_size: 802586232 relation: main_file success: 1 - access_level: open_access checksum: 4c224551adf852b136ec21a4e13f0c1b content_type: application/gzip creator: pub-gitlab-bot date_created: 2021-04-26T09:33:44Z date_updated: 2021-04-26T09:33:44Z file_id: '9353' file_name: MADYPG.zip file_size: 64962865 relation: main_file file_date_updated: 2021-04-26T09:33:44Z gitlab_commit_id: 6a77e7e22769230ae5f5edaa090fb4b828e57573 gitlab_url: https://git.ist.ac.at/gsperl/MADYPG has_accepted_license: '1' license: https://opensource.org/licenses/MIT month: '05' oa: 1 publisher: IST Austria related_material: record: - id: '9818' relation: used_for_analysis_in status: public status: public title: Mechanics-Aware Deformation of Yarn Pattern Geometry (Additional Animation/Model Data) tmp: legal_code_url: https://opensource.org/licenses/MIT name: The MIT License short: MIT type: software user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9 year: '2021' ... --- _id: '8385' abstract: - lang: eng text: 'We present a method for animating yarn-level cloth effects using a thin-shell solver. We accomplish this through numerical homogenization: we first use a large number of yarn-level simulations to build a model of the potential energy density of the cloth, and then use this energy density function to compute forces in a thin shell simulator. We model several yarn-based materials, including both woven and knitted fabrics. Our model faithfully reproduces expected effects like the stiffness of woven fabrics, and the highly deformable nature and anisotropy of knitted fabrics. Our approach does not require any real-world experiments nor measurements; because the method is based entirely on simulations, it can generate entirely new material models quickly, without the need for testing apparatuses or human intervention. We provide data-driven models of several woven and knitted fabrics, which can be used for efficient simulation with an off-the-shelf cloth solver.' acknowledged_ssus: - _id: ScienComp acknowledgement: "We wish to thank the anonymous reviewers and the members of the Visual Computing Group at IST Austria for their valuable feedback. We also thank the creators of the Berkeley Garment Library [de Joya et al. 2012] for providing garment meshes, [Krishnamurthy and Levoy 1996] and [Turk and Levoy 1994] for the armadillo and bunny meshes, the creators of libWetCloth [Fei et al. 2018] for their implementation of discrete elastic rod forces, and Tomáš Skřivan for\r\ninspiring discussions and help with Mathematica code generation. This research was supported by the Scientific Service Units (SSU) of IST Austria through resources provided by Scientific Computing. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 638176. Rahul Narain is supported by a Pankaj Gupta Young Faculty Fellowship and a gift from Adobe Inc." article_number: '48' article_processing_charge: No article_type: original author: - first_name: Georg full_name: Sperl, Georg id: 4DD40360-F248-11E8-B48F-1D18A9856A87 last_name: Sperl - first_name: Rahul full_name: Narain, Rahul last_name: Narain - first_name: Christopher J full_name: Wojtan, Christopher J id: 3C61F1D2-F248-11E8-B48F-1D18A9856A87 last_name: Wojtan orcid: 0000-0001-6646-5546 citation: ama: Sperl G, Narain R, Wojtan C. Homogenized yarn-level cloth. ACM Transactions on Graphics. 2020;39(4). doi:10.1145/3386569.3392412 apa: Sperl, G., Narain, R., & Wojtan, C. (2020). Homogenized yarn-level cloth. ACM Transactions on Graphics. Association for Computing Machinery. https://doi.org/10.1145/3386569.3392412 chicago: Sperl, Georg, Rahul Narain, and Chris Wojtan. “Homogenized Yarn-Level Cloth.” ACM Transactions on Graphics. Association for Computing Machinery, 2020. https://doi.org/10.1145/3386569.3392412. ieee: G. Sperl, R. Narain, and C. Wojtan, “Homogenized yarn-level cloth,” ACM Transactions on Graphics, vol. 39, no. 4. Association for Computing Machinery, 2020. ista: Sperl G, Narain R, Wojtan C. 2020. Homogenized yarn-level cloth. ACM Transactions on Graphics. 39(4), 48. mla: Sperl, Georg, et al. “Homogenized Yarn-Level Cloth.” ACM Transactions on Graphics, vol. 39, no. 4, 48, Association for Computing Machinery, 2020, doi:10.1145/3386569.3392412. short: G. Sperl, R. Narain, C. Wojtan, ACM Transactions on Graphics 39 (2020). date_created: 2020-09-13T22:01:18Z date_published: 2020-07-08T00:00:00Z date_updated: 2024-02-28T12:57:47Z day: '08' ddc: - '000' department: - _id: ChWo doi: 10.1145/3386569.3392412 ec_funded: 1 external_id: isi: - '000583700300021' file: - access_level: open_access checksum: cf4c1d361c3196c4bd424520a5588205 content_type: application/pdf creator: dernst date_created: 2020-11-23T09:01:22Z date_updated: 2020-11-23T09:01:22Z file_id: '8794' file_name: 2020_hylc_submitted.pdf file_size: 38922662 relation: main_file success: 1 file_date_updated: 2020-11-23T09:01:22Z has_accepted_license: '1' intvolume: ' 39' isi: 1 issue: '4' language: - iso: eng main_file_link: - open_access: '1' url: https://doi.org/10.1145/3386569.3392412 month: '07' oa: 1 oa_version: Submitted Version project: - _id: 2533E772-B435-11E9-9278-68D0E5697425 call_identifier: H2020 grant_number: '638176' name: Efficient Simulation of Natural Phenomena at Extremely Large Scales publication: ACM Transactions on Graphics publication_identifier: eissn: - '15577368' issn: - '07300301' publication_status: published publisher: Association for Computing Machinery quality_controlled: '1' related_material: record: - id: '12358' relation: dissertation_contains status: public scopus_import: '1' status: public title: Homogenized yarn-level cloth type: journal_article user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87 volume: 39 year: '2020' ... --- _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 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:10.1109/CVPR.2017.587' apa: 'Rebuffi, S. A., Kolesnikov, A., Sperl, G., & 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. https://doi.org/10.1109/CVPR.2017.587' chicago: 'Rebuffi, Sylvestre Alvise, Alexander Kolesnikov, Georg Sperl, and Christoph Lampert. “ICaRL: Incremental Classifier and Representation Learning,” 2017:5533–42. IEEE, 2017. https://doi.org/10.1109/CVPR.2017.587.' 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. ICaRL: Incremental Classifier and Representation Learning. Vol. 2017, IEEE, 2017, pp. 5533–42, doi:10.1109/CVPR.2017.587.' 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: 2023-09-22T09:51:58Z day: '14' department: - _id: ChLa - _id: ChWo doi: 10.1109/CVPR.2017.587 ec_funded: 1 external_id: 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: c635000d-4b10-11ee-a964-aac5a93f6ac1 volume: 2017 year: '2017' ...