CompRess: Self-Supervised Learning by Compressing Representations
dc.contributor.author | Koohpayegani, Soroush Abbasi | |
dc.contributor.author | Tejankar, Ajinkya | |
dc.contributor.author | Pirsiavash, Hamed | |
dc.date.accessioned | 2020-12-09T18:07:22Z | |
dc.date.available | 2020-12-09T18:07:22Z | |
dc.date.issued | 2020-10-28 | |
dc.description.abstract | Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models. As a result, the gap between supervised and self-supervised learning has been greatly reduced for larger models. In this work, instead of designing a new pseudo task for self-supervised learning, we develop a model compression method to compress an already learned, deep self-supervised model (teacher) to a smaller one (student). We train the student model so that it mimics the relative similarity between the data points in the teacher's embedding space. For AlexNet, our method outperforms all previous methods including the fully supervised model on ImageNet linear evaluation (59.0% compared to 56.5%) and on nearest neighbor evaluation (50.7% compared to 41.4%). To the best of our knowledge, this is the first time a self-supervised AlexNet has outperformed supervised one on ImageNet classification. | en_US |
dc.description.sponsorship | This material is based upon work partially supported by the United States Air Force under Contract No. FA8750-19-C-0098, funding from SAP SE, and also NSF grant number 1845216. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the United States Air Force, DARPA, and other funding agencies. Moreover, we would like to thank Vipin Pillai and Erfan Noury for the valuable initial discussions. We also acknowledge the fruitful comments by all reviewers specifically by Reviewer 2 for suggesting to use teacher’s queue for the student, which improved our results. | en_US |
dc.description.uri | https://arxiv.org/abs/2010.14713 | en_US |
dc.format.extent | 16 pages | en_US |
dc.genre | conference papers and proceedings preprints | en_US |
dc.identifier | doi:10.13016/m2n1nd-9amp | |
dc.identifier.citation | Soroush Abbasi Koohpayegani, Ajinkya Tejankar and Hamed Pirsiavash, CompRess: Self-Supervised Learning by Compressing Representations, https://arxiv.org/abs/2010.14713 | en_US |
dc.identifier.uri | http://hdl.handle.net/11603/20214 | |
dc.language.iso | en_US | en_US |
dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
dc.relation.ispartof | UMBC Faculty Collection | |
dc.relation.ispartof | UMBC Student Collection | |
dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
dc.rights | Attribution 4.0 International | * |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | * |
dc.title | CompRess: Self-Supervised Learning by Compressing Representations | en_US |
dc.type | Text | en_US |