[en] Although supervised learning has been highly successful in improving the state-of-the-art in the domain of image-based computer vision in the past, the margin of improvement has diminished significantly in recent years, indicating that a plateau is in sight. Meanwhile, the use of self-supervised learning (SSL) for the purpose of natural language processing (NLP) has seen tremendous successes during the past couple of years, with this new learning paradigm yielding powerful language models. Inspired by the excellent results obtained in the field of NLP, self-supervised methods that rely on clustering, contrastive learning, distillation, and information-maximization, which all fall under the banner of discriminative SSL, have experienced a swift uptake in the area of computer vision. Shortly afterwards, generative SSL frameworks that are mostly based on masked image modeling, complemented and surpassed the results obtained with discriminative SSL. Consequently, within a span of three years, over 100 unique general-purpose frameworks for generative and discriminative SSL, with a focus on imaging, were proposed. In this survey, we review a plethora of research efforts conducted on image-oriented SSL, providing a historic view and paying attention to best practices as well as useful software packages. While doing so, we discuss pretext tasks for image-based SSL, as well as techniques that are commonly used in image-based SSL. Lastly, to aid researchers who aim at contributing to image-focused SSL, we outline a number of promising research directions.
Precision for document type :
Review article
Disciplines :
Computer science
Author, co-author :
Ozbulak, Utku; Ghent University, Belgium ; Ghent University Global Campus, South Korea
Lee, Hyun Jung; Ghent University, Belgium ; Ghent University Global Campus, South Korea
Boga, Beril; BSH Hausgeräte GmbH, Germany
Anzaku, Esla Timothy; Ghent University, Belgium ; Ghent University Global Campus, South Korea
Park, Homin; Ghent University, Belgium ; Ghent University Global Campus, South Korea
Van Messem, Arnout ; Université de Liège - ULiège > Département de mathématique > Statistique appliquée aux sciences
De Neve, Wesley; UGent - Universiteit Gent [BE] ; Ghent University Global Campus
Vankerschaver, Joris; UGent - Ghent University [BE] ; Ghent University Global Campus
Language :
English
Title :
Know Your Self-supervised Learning: A Survey on Image-based Generative and Discriminative Training
Saleh Albelwi. Survey on self-supervised learning: auxiliary pretext tasks and contrastive learning methods in imaging. Entropy, 24(4):551, 2022.
Elad Amrani and Alex Bronstein. Self-supervised classification network. arXiv preprint arXiv:2103.10994, 2021.
Yuki Markus Asano, Christian Rupprecht, and Andrea Vedaldi. Self-labelling via simultaneous clustering and representation learning. arXiv preprint arXiv:1911.05371, 2019.
Mido Assran, Randall Balestriero, Quentin Duval, Florian Bordes, Ishan Misra, Piotr Bojanowski, Pascal Vincent, Michael Rabbat, and Nicolas Ballas. The hidden uniform cluster prior in self-supervised learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=04K3PMtMckp.
Philip Bachman. Going meta: learning algorithms and the self-supervised machine. In Microsoft AI Podcast, 2019. URL https://www.youtube.com/watch?v=CSjWb3gcZJ4.
Philip Bachman, R Devon Hjelm, and William Buchwalter. Learning representations by maximizing mutual information across views. Advances in Neural Information Processing Systems, 32, 2019.
Alexei Baevski, Wei-Ning Hsu, Qiantong Xu, Arun Babu, Jiatao Gu, and Michael Auli. Data2vec: A general framework for self-supervised learning in speech, vision and language. In International Conference on Machine Learning, pp. 1298–1312. PMLR, 2022.
Hangbo Bao, Li Dong, Songhao Piao, and Furu Wei. BEiT: Bert pre-training of image transformers. arXiv preprint arXiv:2106.08254, 2021.
Adrien Bardes, Jean Ponce, and Yann LeCun. VICReg: Variance-invariance-covariance regularization for self-supervised learning. arXiv preprint arXiv:2105.04906, 2021.
Adrien Bardes, Jean Ponce, and Yann LeCun. VICRegL: Self-supervised learning of local visual features. arXiv preprint arXiv:2210.01571, 2022.
Gulcin Baykal and Gozde Unal. Deshufflegan: A self-supervised gan to improve structure learning. In 2020 IEEE International Conference on Image Processing (ICIP), pp. 708–712. IEEE, 2020.
Gulcin Baykal, Furkan Ozcelik, and Gozde Unal. Exploring deshufflegans in self-supervised generative adversarial networks. Pattern Recognition, 122:108244, 2022.
Suzanna Becker and Geoffrey E Hinton. Self-organizing neural network that discovers surfaces in random-dot stereograms. Nature, 355(6356):161–163, 1992.
Yoshua Bengio, Aaron Courville, and Pascal Vincent. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence, 35(8):1798–1828, 2013.
Marcelo Bertalmio, Guillermo Sapiro, Vincent Caselles, and Coloma Ballester. Image inpainting. In Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 417–424, 2000.
Luca Bertinetto, Jack Valmadre, Joao F Henriques, Andrea Vedaldi, and Philip HS Torr. Fully-convolutional siamese networks for object tracking. In European Conference on Computer Vision, pp. 850–865. Springer, 2016.
Christopher M. Bishop. Pattern Recognition and Machine Learning. Springer, 2006.
Andrew Brock, Jeff Donahue, and Karen Simonyan. Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096, 2018.
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. Signature verification using a “siamese” time delay neural network. Advances in Neural Information Processing Systems, 6, 1993.
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33:1877–1901, 2020.
Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, and Han Hu. Parametric instance classification for unsupervised visual feature learning. Advances in Neural Information Processing Systems, 33:15614–15624, 2020.
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi. Domain generalization by solving jigsaw puzzles. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2229–2238, 2019.
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze. Deep clustering for unsupervised learning of visual features. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 132–149, 2018.
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin. Unsupervised pre-training of image features on non-curated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 2959–2968, 2019.
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems, 33:9912–9924, 2020.
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. Emerging properties in self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9650–9660, 2021.
M Emre Celebi and Kemal Aydin. Unsupervised learning algorithms. Springer, 2016.
Souradip Chakraborty, Aritra Roy Gosthipaty, and Sayak Paul. G-SimCLR: Self-supervised contrastive learning with guided projection via pseudo labelling. In 2020 International Conference on Data Mining Workshops (ICDMW), pp. 912–916. IEEE, 2020.
Guillaume Charpiat, Matthias Hofmann, and Bernhard Schölkopf. Automatic image colorization via multimodal predictions. In European Conference on Computer Vision, pp. 126–139. Springer, 2008.
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. Reading Wikipedia to answer open-domain questions. arXiv preprint arXiv:1704.00051, 2017.
Mark Chen, Alec Radford, Rewon Child, Jeffrey Wu, Heewoo Jun, David Luan, and Ilya Sutskever. Generative pretraining from pixels. In International conference on machine learning, pp. 1691–1703. PMLR, 2020a.
Qingzhong Chen, Shilun Cai, Crystal Cai, Zefang Yu, Dahong Qian, and Suncheng Xiang. Coloscrl: Self-supervised contrastive representation learning for colonoscopic video retrieval. arXiv preprint arXiv:2303.15671, 2023a.
Ting Chen. Advancing self-supervised and semi-supervised learning with SimCLR. In Google AI Blog, 2020. URL https://ai.googleblog.com/2020/04/advancing-self-supervised-and-semi.html.
Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, and Neil Houlsby. Self-supervised GANs via auxiliary rotation loss. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12154–12163, 2019.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. A simple framework for contrastive learning of visual representations. In International Conference on Machine Learning, pp. 1597–1607. PMLR, 2020b.
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E Hinton. Big selfsupervised models are strong semi-supervised learners. Advances in Neural Information Processing Systems, 33:22243–22255, 2020c.
Xinlei Chen and Kaiming He. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15750–15758, 2021.
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He. Improved baselines with momentum contrastive learning. arXiv preprint arXiv:2003.04297, 2020d.
Xinlei Chen, Saining Xie, and Kaiming He. An empirical study of training self-supervised vision transformers. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9640–9649, 2021.
Yabo Chen, Yuchen Liu, Dongsheng Jiang, Xiaopeng Zhang, Wenrui Dai, Hongkai Xiong, and Qi Tian. Sdae: Self-distillated masked autoencoder. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX, pp. 108–124. Springer, 2022b.
Yubei Chen, Adrien Bardes, Zengyi Li, and Yann LeCun. Intra-instance VICReg: Bag of self-supervised image patch embedding explains the performance. 2023b. URL https://openreview.net/forum?id= J923QzIz8Sh.
Ziwei Chen, Qiang Li, Xiaofeng Wang, and Wankou Yang. LiftedCL: Lifting contrastive learning for humancentric perception. In The Eleventh International Conference on Learning Representations, 2023c.
Zezhou Cheng, Qingxiong Yang, and Bin Sheng. Deep colorization. In Proceedings of the IEEE international conference on computer vision, pp. 415–423, 2015.
Davide Chicco. Siamese neural networks: An overview. Artificial Neural Networks, pp. 73–94, 2021.
Sumit Chopra, Raia Hadsell, and Yann LeCun. Learning a similarity metric discriminatively, with application to face verification. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), volume 1, pp. 539–546. IEEE, 2005.
Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, and Stefanie Jegelka. contrastive learning. Advances in Neural Information Processing Systems, 33:8765–8775, 2020. Debiased
Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. Fast And accurate deep network learning by exponential linear units (Elus). arXiv preprint arXiv:1511.07289, 2015.
Adam Coates and Andrew Y Ng. Learning feature representations with k-means. In Neural Networks: Tricks of the Trade, pp. 561–580. Springer, 2012.
Adam Coates, Andrew Ng, and Honglak Lee. An analysis of single-layer networks in unsupervised feature learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pp. 215–223. JMLR Workshop and Conference Proceedings, 2011.
Marco Cuturi. Sinkhorn distances: Lightspeed computation of optimal transport. Advances in Neural Information Processing Systems, 26, 2013.
Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci. solo-learn: A library of self-supervised methods for visual representation learning. Journal of Machine Learning Research, 23 (56):1–6, 2022. URL http://jmlr.org/papers/v23/21-1155.html.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.
Carl Doersch, Abhinav Gupta, and Alexei A Efros. Unsupervised visual representation learning by context prediction. In Proceedings of the IEEE international conference on computer vision, pp. 1422–1430, 2015.
Jeff Donahue and Karen Simonyan. Large scale adversarial representation learning. Advances in neural information processing systems, 32, 2019.
Jeff Donahue, Philipp Krähenbühl, and Trevor Darrell. Adversarial feature learning. arXiv preprint arXiv:1605.09782, 2016.
Xiaoyi Dong, Jianmin Bao, Ting Zhang, Dongdong Chen, Weiming Zhang, Lu Yuan, Dong Chen, Fang Wen, and Nenghai Yu. Peco: Perceptual codebook for bert pre-training of vision transformers. arXiv preprint arXiv:2111.12710, 2021.
Alexey Dosovitskiy, Jost Tobias Springenberg, Martin Riedmiller, and Thomas Brox. Discriminative unsupervised feature learning with convolutional neural networks. Advances in Neural Information Processing Systems, 27, 2014.
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020.
Vincent Dumoulin, Ishmael Belghazi, Ben Poole, Olivier Mastropietro, Alex Lamb, Martin Arjovsky, and Aaron Courville. Adversarially learned inference. arXiv preprint arXiv:1606.00704, 2016.
Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. With a little help from my friends: Nearest-neighbor contrastive learning of visual representations. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9588–9597, 2021.
Alyosha Efros. The gelato bet. 2019. URL https://people.eecs.berkeley.edu/~efros/gelato_bet. html.
Aleksandr Ermolov, Aliaksandr Siarohin, Enver Sangineto, and Nicu Sebe. Whitening for self-supervised representation learning. In International Conference on Machine Learning, pp. 3015–3024. PMLR, 2021.
Patrick Esser, Robin Rombach, and Bjorn Ommer. Taming transformers for high-resolution image synthesis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 12873–12883, 2021.
M. Everingham, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results. http://www.pascalnetwork.org/challenges/VOC/voc2007/workshop/index.html, 2007.
Yuxin Fang, Li Dong, Hangbo Bao, Xinggang Wang, and Furu Wei. Corrupted image modeling for selfsupervised visual pre-training. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=09hVcSDkea.
Zhiyuan Fang, Jianfeng Wang, Lijuan Wang, Lei Zhang, Yezhou Yang, and Zicheng Liu. Seed: Self-supervised distillation for visual representation. arXiv preprint arXiv:2101.04731, 2021.
Abe Fetterman and Josh Albrecht. Understanding self-supervised and contrastive learning with “bootstrap your own latent” (BYOL). In Generally Intelligent AI Blog, 2020. URL https://generallyintelligent. ai/blog/2020-08-24-understanding-self-supervised-contrastive-learning/.
Peng Gao, Renrui Zhang, Hongyang Li, Hongsheng Li, and Yu Qiao. Mimic before reconstruct: Enhance masked autoencoders with feature mimicking, 2023. URL https://openreview.net/forum?id= UoBJm4V21md.
Yuting Gao, Jia-Xin Zhuang, Ke Li, Hao Cheng, Xiaowei Guo, Feiyue Huang, Rongrong Ji, and Xing Sun. Disco: Remedy self-supervised learning on lightweight models with distilled contrastive learning. arXiv preprint arXiv:2104.09124, 2021.
Quentin Garrido, Randall Balestriero, Laurent Najman, and Yann Lecun. Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank. arXiv preprint arXiv:2210.02885, 2022a.
Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, and Yann Lecun. On the duality between contrastive and non-contrastive self-supervised learning. arXiv preprint arXiv:2206.02574, 2022b.
Spyros Gidaris, Praveer Singh, and Nikos Komodakis. Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728, 2018.
Spyros Gidaris, Andrei Bursuc, Gilles Puy, Nikos Komodakis, Matthieu Cord, and Patrick Pérez. Online bag-of-visual-words generation for unsupervised representation learning. arXiv preprint arXiv:2012.11552, 2021.
Anupriya Gogna and Angshul Majumdar. Semi supervised autoencoder. In International Conference on Neural Information Processing, pp. 82–89. Springer, 2016.
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Communications of the ACM, 63(11): 139–144, 2020.
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, et al. Self-supervised pretraining of visual features in the wild. arXiv preprint arXiv:2103.01988, 2021a.
Priya Goyal, Quentin Duval, Jeremy Reizenstein, Matthew Leavitt, Min Xu, Benjamin Lefaudeux, Mannat Singh, Vinicius Reis, Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Ishan Misra. VISSL. https://github.com/facebookresearch/vissl, 2021b.
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Guo, Mohammad Gheshlaghi Azar, et al. Bootstrap your own latent – a new approach to self-supervised learning. Advances in Neural Information Processing Systems, 33:21271–21284, 2020.
Michael Gutmann and Aapo Hyvärinen. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pp. 297–304. JMLR Workshop and Conference Proceedings, 2010.
Raia Hadsell, Sumit Chopra, and Yann LeCun. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), volume 2, pp. 1735–1742. IEEE, 2006.
Will Hamilton, Zhitao Ying, and Jure Leskovec. Inductive representation learning on large graphs. Advances in Neural Information Processing Systems, 30, 2017.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. Momentum contrast for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729–9738, 2020.
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000–16009, 2022.
Olivier Henaff. Data-efficient image recognition with contrastive predictive coding. In International Conference on Machine Learning, pp. 4182–4192. PMLR, 2020.
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song. Using self-supervised learning can improve model robustness and uncertainty. Advances in Neural Information Processing Systems, 32, 2019.
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. GANs trained by a two time-scale update rule converge to a local Nash equilibrium. Advances in neural information processing systems, 30, 2017.
Geoffrey Hinton, Oriol Vinyals, Jeff Dean, et al. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2(7), 2015.
Geoffrey E Hinton, Simon Osindero, and Yee-Whye Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):1527–1554, 2006.
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. Learning deep representations by mutual information estimation and maximization. arXiv preprint arXiv:1808.06670, 2018.
Liang Hou, Huawei Shen, Qi Cao, and Xueqi Cheng. Self-supervised GANs with label augmentation. Advances in Neural Information Processing Systems, 34:13019–13031, 2021.
Zejiang Hou, Fei Sun, Yen-Kuang Chen, Yuan Xie, and Sun-Yuan Kung. Milan: Masked image pretraining on language assisted representation. arXiv preprint arXiv:2208.06049, 2022.
Jeremy Howard. Self-supervised learning and computer vision. In fast.ai Blog, 2020. URL https://www. fast.ai/2020/01/13/self_supervised/.
Qianjiang Hu, Xiao Wang, Wei Hu, and Guo-Jun Qi. Adco: Adversarial contrast for efficient learning of unsupervised representations from self-trained negative adversaries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1074–1083, 2021.
Tianyang Hu, Zhili LIU, Fengwei Zhou, Wenjia Wang, and Weiran Huang. Your contrastive learning is secretly doing stochastic neighbor embedding. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=XFSCKELP3bp.
Tianyu Hua, Wenxiao Wang, Zihui Xue, Sucheng Ren, Yue Wang, and Hang Zhao. On feature decorrelation in self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9598–9608, 2021.
Ka Yu Hui. Direct modeling of complex invariances for visual object features. In International Conference on Machine Learning, pp. 352–360. PMLR, 2013.
Satoshi Iizuka, Edgar Simo-Serra, and Hiroshi Ishikawa. Let there be color! Joint end-to-end learning of global and local image priors for automatic image colorization with simultaneous classification. ACM Transactions on Graphics (ToG), 35(4):1–11, 2016.
Sergey Ioffe and Christian Szegedy. Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR, abs/1502.03167, 2015.
Saachi Jain, Hadi Salman, Alaa Khaddaj, Eric Wong, Sung Min Park, and Aleksander Madry. A data-based perspective on transfer learning. arXiv preprint arXiv:2207.05739, 2022.
Li Jing, Pascal Vincent, Yann LeCun, and Yuandong Tian. Understanding dimensional collapse in contrastive self-supervised learning. arXiv preprint arXiv:2110.09348, 2021.
Daniel D. Johnson, Ayoub El Hanchi, and Chris J. Maddison. Contrastive learning can find an optimal basis for approximately view-invariant functions. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=AjC0KBjiMu.
Armand Joulin, Laurens van der Maaten, Allan Jabri, and Nicolas Vasilache. Learning visual features from large weakly supervised data. In European Conference on Computer Vision, pp. 67–84. Springer, 2016.
Yannis Kalantidis, Mert Bulent Sariyildiz, Noe Pion, Philippe Weinzaepfel, and Diane Larlus. Hard negative mixing for contrastive learning. Advances in Neural Information Processing Systems, 33:21798–21809, 2020.
Yannis Kalantidis, Carlos Lassance, Jon Almazan, and Diane Larlus. TLDR: Twin learning for dimensionality reduction. arXiv preprint arXiv:2110.09455, 2021.
Angjoo Kanazawa, David W Jacobs, and Manmohan Chandraker. Warpnet: Weakly supervised matching for single-view reconstruction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3253–3261, 2016.
Adnan Khan, Sarah AlBarri, and Muhammad Arslan Manzoor. Contrastive self-supervised learning: a survey on different architectures. In 2022 2nd International Conference on Artificial Intelligence (ICAI), pp. 1–6. IEEE, 2022.
Diederik P. Kingma and Jimmy Ba. Adam: A method for stochastic optimization. CoRR, abs/1412.6980, 2014.
Diederik P Kingma and Max Welling. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, and Hamed Pirsiavash. Mean shift for self-supervised learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10326– 10335, 2021.
Alex Krizhevsky and Geoffrey Hinton. Learning multiple layers of features from tiny images. Technical report, Citeseer, 2009.
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. ImageNet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 2012.
Anna Kukleva, Moritz Böhle, Bernt Schiele, Hilde Kuehne, and Christian Rupprecht. Temperature schedules for self-supervised contrastive methods on long-tail data. arXiv preprint arXiv:2303.13664, 2023.
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. Learning representations for automatic colorization. In European Conference on Computer Vision, pp. 577–593. Springer, 2016.
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich. Colorization as a proxy task for visual understanding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6874–6883, 2017.
Samuel Lavoie, Christos Tsirigotis, Max Schwarzer, Ankit Vani, Michael Noukhovitch, Kenji Kawaguchi, and Aaron Courville. Simplicial embeddings in self-supervised learning and downstream classification. arXiv preprint arXiv:2204.00616, 2022.
Yann LeCun. I now call it “self-supervised learning”. 2019. URL https://www.facebook.com/yann.lecun/posts/10155934004262143.
Yann LeCun. Self-supervised learning. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI, Invited Talk, 2020. URL https://drive.google.com/file/d/1r-mDL4IX_hzZLDBKp8_ e8VZqD7fOzBkF/view.
Yann LeCun and Ishan Misra. Self-supervised learning: The dark matter of intelligence. In Facebook AI Blog, 2020. URL https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/.
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998.
Kuang-Huei Lee, Anurag Arnab, Sergio Guadarrama, John Canny, and Ian Fischer. Compressive visual representations. Advances in Neural Information Processing Systems, 34:19538–19552, 2021.
Alexander C Li, Alexei A Efros, and Deepak Pathak. Understanding collapse in non-contrastive siamese representation learning. In European Conference on Computer Vision, pp. 490–505. Springer, 2022a.
Chunyuan Li, Xiujun Li, Lei Zhang, Baolin Peng, Mingyuan Zhou, and Jianfeng Gao. Self-supervised pre-training with hard examples improves visual representations. arXiv preprint arXiv:2012.13493, 2020a.
Chunyuan Li, Jianwei Yang, Pengchuan Zhang, Mei Gao, Bin Xiao, Xiyang Dai, Lu Yuan, and Jianfeng Gao. Efficient self-supervised vision transformers for representation learning. arXiv preprint arXiv:2106.09785, 2021a.
Jin Li, Yaoming Wang, Xiaopeng Zhang, Yabo Chen, Dongsheng Jiang, Wenrui Dai, Chenglin Li, Hongkai Xiong, and Qi Tian. Progressively compressed auto-encoder for self-supervised representation learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview. net/forum?id=8T4qmZbTkW7.
Junnan Li, Pan Zhou, Caiming Xiong, and Steven CH Hoi. Prototypical contrastive learning of unsupervised representations. arXiv preprint arXiv:2005.04966, 2020b.
Ru Li, Shuaicheng Liu, Guangfu Wang, Guanghui Liu, and Bing Zeng. JigsawGAN: Auxiliary learning for solving jigsaw puzzles with generative adversarial networks. IEEE Transactions on Image Processing, 31: 513–524, 2021b.
Zhaowen Li, Yousong Zhu, Fan Yang, Wei Li, Chaoyang Zhao, Yingying Chen, Zhiyang Chen, Jiahao Xie, Liwei Wu, Rui Zhao, et al. Univip: A unified framework for self-supervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14627–14636, 2022b.
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft Coco: Common Objects In Context. In Proceedings of the IEEE European Conference on Computer Vision, pp. 740–755. Springer, 2014.
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976–11986, 2022a.
Ziwen Liu, Bonan Li, Congying Han, Tiande Guo, and Xuecheng Nie. Masked reconstruction contrastive learning with information bottleneck principle. arXiv preprint arXiv:2211.09013, 2022b.
Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017.
Qing Luan, Fang Wen, Daniel Cohen-Or, Lin Liang, Ying-Qing Xu, and Heung-Yeung Shum. Natural image colorization. In Proceedings of the 18th Eurographics conference on Rendering Techniques, pp. 309–320, 2007.
Mario Lucic, Michael Tschannen, Marvin Ritter, Xiaohua Zhai, Olivier Bachem, and Sylvain Gelly. Highfidelity image generation with fewer labels. In International conference on machine learning, pp. 4183–4192. PMLR, 2019.
Shlok Mishra, Anshul Shah, Ankan Bansal, Abhyuday Jagannatha, Abhishek Sharma, David Jacobs, and Dilip Krishnan. Object-aware cropping for self-supervised learning. arXiv preprint arXiv:2112.00319, 2021.
Shlok Kumar Mishra, Joshua David Robinson, Huiwen Chang, David Jacobs, Weicheng Kuo, Aaron Sarna, Aaron Maschinot, and Dilip Krishnan. CAN: A simple, efficient and scalable contrastive masked autoencoder framework for learning visual representations, 2023. URL https://openreview.net/forum?id= qmV_tOHp7B9.
Ishan Misra and Laurens van der Maaten. Self-supervised learning of pretext-invariant representations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6707–6717, 2020.
Jovana Mitrovic, Brian McWilliams, Jacob Walker, Lars Buesing, and Charles Blundell. Representation learning via invariant causal mechanisms. arXiv preprint arXiv:2010.07922, 2020.
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng. Reading digits in natural images with unsupervised feature learning. 2011.
Mehdi Noroozi and Paolo Favaro. Unsupervised learning of visual representations by solving jigsaw puzzles. In European Conference on Computer Vision, pp. 69–84. Springer, 2016.
Mehdi Noroozi, Hamed Pirsiavash, and Paolo Favaro. Representation learning by learning to count. In Proceedings of the IEEE international conference on computer vision, pp. 5898–5906, 2017.
David Novotny, Samuel Albanie, Diane Larlus, and Andrea Vedaldi. Self-supervised learning of geometrically stable features through probabilistic introspection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3637–3645, 2018.
Aaron van den Oord, Yazhe Li, and Oriol Vinyals. coding. arXiv preprint arXiv:1807.03748, 2018. Representation learning with contrastive predictive
Bo Pang, Yifan Zhang, Yaoyi Li, Jia Cai, and Cewu Lu. Unsupervised visual representation learning by synchronous momentum grouping. In Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXX, pp. 265–282. Springer, 2022.
Kaiyue Pang, Yongxin Yang, Timothy M Hospedales, Tao Xiang, and Yi-Zhe Song. Solving mixed-modal jigsaw puzzle for fine-grained sketch-based image retrieval. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10347–10355, 2020.
Namuk Park, Wonjae Kim, Byeongho Heo, Taekyung Kim, and Sangdoo Yun. What do self-supervised vision transformers learn? In The Eleventh International Conference on Learning Representations, 2023.
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros. Context encoders: Feature learning by inpainting. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2536–2544, 2016.
Zhiliang Peng, Li Dong, Hangbo Bao, Qixiang Ye, and Furu Wei. BEiT v2: Masked image modeling with vector-quantized visual tokenizers. arXiv preprint arXiv:2208.06366, 2022.
Qi Qian, Yuanhong Xu, Juhua Hu, Hao Li, and Rong Jin. Unsupervised visual representation learning by online constrained k-means. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16640–16649, 2022.
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervision. In International Conference on Machine Learning, pp. 8748–8763. PMLR, 2021.
Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. Zero-shot text-to-image generation. In International Conference on Machine Learning, pp. 8821–8831. PMLR, 2021.
Sanat Ramesh, Vinkle Srivastav, Deepak Alapatt, Tong Yu, Aditya Murali, Luca Sestini, Chinedu Innocent Nwoye, Idris Hamoud, Antoine Fleurentin, Georgios Exarchakis, et al. Dissecting self-supervised learning methods for surgical computer vision. arXiv preprint arXiv:2207.00449, 2022.
Sucheng Ren, Fangyun Wei, Zheng Zhang, and Han Hu. TinyMIM: An empirical study of distilling MIM pre-trained models. arXiv preprint arXiv:2301.01296, 2023.
Zhongzheng Ren and Yong Jae Lee. Cross-domain self-supervised multi-task feature learning using synthetic imagery. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 762–771, 2018.
Pierre H Richemond, Jean-Bastien Grill, Florent Altché, Corentin Tallec, Florian Strub, Andrew Brock, Samuel Smith, Soham De, Razvan Pascanu, Bilal Piot, et al. BYOL works even without batch statistics. arXiv preprint arXiv:2010.10241, 2020.
Joshua Robinson, Ching-Yao Chuang, Suvrit Sra, and Stefanie Jegelka. negative samples. arXiv preprint arXiv:2010.04592, 2020. Contrastive learning with hard
Ignacio Rocco, Relja Arandjelovic, and Josef Sivic. Convolutional neural network architecture for geometric matching. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6148–6157, 2017.
Yangjun Ruan, Saurabh Singh, Warren Morningstar, Alexander A Alemi, Sergey Ioffe, Ian Fischer, and Joshua V Dillon. Weighted ensemble self-supervised learning. arXiv preprint arXiv:2211.09981, 2022.
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3):211–252, 2015.
Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra, David Jacobs, Anoop Cherian, and Rama Chellappa. HaLP: Hallucinating latent positives for skeleton-based self-supervised learning of actions. arXiv preprint arXiv:2304.00387, 2023.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. International Conference on Learning Representations, 2015.
Kihyuk Sohn. Improved deep metric learning with multi-class n-pair loss objective. Advances in Neural Information Processing Systems, 29, 2016.
Kihyuk Sohn and Honglak Lee. Learning invariant representations with local transformations. arXiv preprint arXiv:1206.6418, 2012.
Igor Susmelj, Matthias Heller, Philipp Wirth, Jeremy Prescott, and Malte Ebner et al. Lightly. GitHub. Note: https://github.com/lightly-ai/lightly, 2020.
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1–9, 2015.
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2016.
Chuanqi Tan, Fuchun Sun, Tao Kong, Wenchang Zhang, Chao Yang, and Chunfang Liu. A survey on deep transfer learning. In International Conference on Artificial Neural Networks, pp. 270–279. Springer, 2018.
Chenxin Tao, Honghui Wang, Xizhou Zhu, Jiahua Dong, Shiji Song, Gao Huang, and Jifeng Dai. Exploring the equivalence of siamese self-supervised learning via a unified gradient framework. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14431–14440, 2022a.
Antti Tarvainen and Harri Valpola. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in Neural Information Processing Systems, 30, 2017.
Keyu Tian, Yi Jiang, Qishuai Diao, Chen Lin, Liwei Wang, and Zehuan Yuan. Designing bert for convolutional networks: Sparse and hierarchical masked modeling. arXiv preprint arXiv:2301.03580, 2023.
Yonglong Tian, Dilip Krishnan, and Phillip Isola. Contrastive multiview coding. In European Conference on Computer Vision, pp. 776–794. Springer, 2020a.
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola. What makes for good views for contrastive learning? Advances in Neural Information Processing Systems, 33:6827–6839, 2020b.
Yonglong Tian, Olivier J Henaff, and Aäron van den Oord. Divide and contrast: Self-supervised learning from uncurated data. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10063–10074, 2021a.
Yuandong Tian. Understanding the role of nonlinearity in training dynamics of contrastive learning. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=s130rTE3U_X.
Yuandong Tian, Lantao Yu, Xinlei Chen, and Surya Ganguli. Understanding self-supervised learning with dual deep networks. arXiv preprint arXiv:2010.00578, 2020c.
Yuandong Tian, Xinlei Chen, and Surya Ganguli. Understanding self-supervised learning dynamics without contrastive pairs. In International Conference on Machine Learning, pp. 10268–10278. PMLR, 2021b.
Nenad Tomasev, Ioana Bica, Brian McWilliams, Lars Buesing, Razvan Pascanu, Charles Blundell, and Jovana Mitrovic. Pushing the limits of self-supervised ResNets: Can we outperform supervised learning without labels on ImageNet? arXiv preprint arXiv:2201.05119, 2022.
Trieu H Trinh, Minh-Thang Luong, and Quoc V Le. Selfie: Self-supervised pretraining for image embedding. arXiv preprint arXiv:1906.02940, 2019.
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool. Scan: Learning to classify images without labels. In European Conference on Computer Vision, pp. 268–285. Springer, 2020.
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, and Pierre-Antoine Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th International Conference on Machine Learning, pp. 1096–1103, 2008.
Feng Wang, Tao Kong, Rufeng Zhang, Huaping Liu, and Hang Li. Self-supervised learning by estimating twin class distributions. arXiv preprint arXiv:2110.07402, 2021a.
Guangrun Wang, Keze Wang, Guangcong Wang, Philip HS Torr, and Liang Lin. Solving inefficiency of self-supervised representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9505–9515, 2021b.
Wenhui Wang, Hangbo Bao, Li Dong, Johan Bjorck, Zhiliang Peng, Qiang Liu, Kriti Aggarwal, Owais Khan Mohammed, Saksham Singhal, Subhojit Som, et al. Image as a foreign language: BEiT pretraining for all vision and vision-language tasks. arXiv preprint arXiv:2208.10442, 2022a.
Xiao Wang and Guo-Jun Qi. Contrastive learning with stronger augmentations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
Xiao Wang, Yuhang Huang, Dan Zeng, and Guo-Jun Qi. Caco: Both positive and negative samples are directly learnable via cooperative-adversarial contrastive learning. arXiv preprint arXiv:2203.14370, 2022b.
Xinlong Wang, Rufeng Zhang, Chunhua Shen, Tao Kong, and Lei Li. Dense contrastive learning for selfsupervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024–3033, 2021c.
Yidong Wang, Hao Chen, Yue Fan, Wang Sun, Ran Tao, Wenxin Hou, Renjie Wang, Linyi Yang, Zhi Zhou, Lan-Zhe Guo, Heli Qi, Zhen Wu, Yu-Feng Li, Satoshi Nakamura, Wei Ye, Marios Savvides, Bhiksha Raj, Takahiro Shinozaki, Bernt Schiele, Jindong Wang, Xing Xie, and Yue Zhang. USB: A unified semi-supervised learning benchmark for classification. In Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022c. doi: 10.48550/arxiv.2208.07204. URL https://arxiv.org/abs/2208.07204.
Zhaoqing Wang, Qiang Li, Guoxin Zhang, Pengfei Wan, Wen Zheng, Nannan Wang, Mingming Gong, and Tongliang Liu. Exploring set similarity for dense self-supervised representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16590–16599, 2022d.
Zhaoqing Wang, Ziyu Chen, Yaqian Li, Yandong Guo, Jun Yu, Mingming Gong, and Tongliang Liu. Mosaic representation learning for self-supervised visual pre-training. In The Eleventh International Conference on Learning Representations, 2023.
Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer. Masked feature prediction for self-supervised visual pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14668–14678, 2022.
Bichen Wu, Chenfeng Xu, Xiaoliang Dai, Alvin Wan, Peizhao Zhang, Zhicheng Yan, Masayoshi Tomizuka, Joseph Gonzalez, Kurt Keutzer, and Peter Vajda. Visual transformers: Token-based image representation and processing for computer vision. arXiv preprint arXiv:2006.03677, 2020.
Zhirong Wu, Alexei A Efros, and Stella X Yu. Improving generalization via scalable neighborhood component analysis. In Proceedings of the European Conference on Computer Vision (ECCV), pp. 685–701, 2018a.
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin. Unsupervised feature learning via non-parametric instance discrimination. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3733–3742, 2018b.
Tete Xiao, Xiaolong Wang, Alexei A Efros, and Trevor Darrell. What should not be contrastive in contrastive learning. arXiv preprint arXiv:2008.05659, 2020.
Tete Xiao, Colorado J Reed, Xiaolong Wang, Kurt Keutzer, and Trevor Darrell. Region similarity representation learning. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10539–10548, 2021.
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, and Chen Change Loy. Unsupervised object-level representation learning from scene images. Advances in Neural Information Processing Systems, 34:28864– 28876, 2021a.
Jiahao Xie, Xiaohang Zhan, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy. Delving into inter-image invariance for unsupervised visual representations. International Journal of Computer Vision, pp. 1–20, 2022a.
Zhenda Xie, Yutong Lin, Zhuliang Yao, Zheng Zhang, Qi Dai, Yue Cao, and Han Hu. Self-supervised learning with swin transformers. arXiv preprint arXiv:2105.04553, 2021b.
Zhenda Xie, Yutong Lin, Zheng Zhang, Yue Cao, Stephen Lin, and Han Hu. Propagate yourself: Exploring pixel-level consistency for unsupervised visual representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16684–16693, 2021c.
Zhenda Xie, Zheng Zhang, Yue Cao, Yutong Lin, Jianmin Bao, Zhuliang Yao, Qi Dai, and Han Hu. Simmim: A simple framework for masked image modeling. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9653–9663, 2022b.
Haohang Xu, Jiemin Fang, Xiaopeng Zhang, Lingxi Xie, Xinggang Wang, Wenrui Dai, Hongkai Xiong, and Qi Tian. Bag of instances aggregation boosts self-supervised distillation. In International Conference on Learning Representations, 2021.
Linli Xu, James Neufeld, Bryce Larson, and Dale Schuurmans. Maximum margin clustering. Advances in Neural Information Processing Systems, 17, 2004.
Chao Yang, Xin Lu, Zhe Lin, Eli Shechtman, Oliver Wang, and Hao Li. High-resolution image inpainting using multi-scale neural patch synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6721–6729, 2017.
Jianwei Yang, Devi Parikh, and Dhruv Batra. Joint unsupervised learning of deep representations and image clusters. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5147–5156, 2016.
Kun Yi, Yixiao Ge, Xiaotong Li, Shusheng Yang, Dian Li, Jianping Wu, Ying Shan, and Xiaohu Qie. Masked image modeling with denoising contrast. arXiv preprint arXiv:2205.09616, 2022.
Jiahui Yu, Zhe Lin, Jimei Yang, Xiaohui Shen, Xin Lu, and Thomas S Huang. Free-form image inpainting with gated convolution. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 4471–4480, 2019.
Jiahui Yu, Xin Li, Jing Yu Koh, Han Zhang, Ruoming Pang, James Qin, Alexander Ku, Yuanzhong Xu, Jason Baldridge, and Yonghui Wu. Vector-quantized image modeling with improved VQGAN. arXiv preprint arXiv:2110.04627, 2021.
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo. Cutmix: Regularization strategy to train strong classifiers with localizable features. In Proceedings of the IEEE/CVF international conference on computer vision, pp. 6023–6032, 2019.
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny. Barlow twins: Self-supervised learning via redundancy reduction. In International Conference on Machine Learning, pp. 12310–12320. PMLR, 2021.
Xiaohang Zhan, Jiahao Xie, Ziwei Liu, Yew-Soon Ong, and Chen Change Loy. Online deep clustering for unsupervised representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 6688–6697, 2020.
Chaoning Zhang, Kang Zhang, Trung X Pham, Axi Niu, Zhinan Qiao, Chang D Yoo, and In So Kweon. Dual temperature helps contrastive learning without many negative samples: Towards understanding and simplifying MoCo. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14441–14450, 2022a.
Liheng Zhang, Guo-Jun Qi, Liqiang Wang, and Jiebo Luo. AET vs. AED: Unsupervised representation learning by auto-encoding transformations rather than data. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2547–2555, 2019.
Richard Zhang, Phillip Isola, and Alexei A Efros. Colorful image colorization. In European Conference on Computer Vision, pp. 649–666. Springer, 2016.
Richard Zhang, Phillip Isola, and Alexei A Efros. Split-brain autoencoders: Unsupervised learning by cross-channel prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1058–1067, 2017.
Shaofeng Zhang, Lyn Qiu, Feng Zhu, Junchi Yan, Hengrui Zhang, Rui Zhao, Hongyang Li, and Xiaokang Yang. Align representations with base: A new approach to self-supervised learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16600–16609, 2022b.
Tong Zhang, Congpei Qiu, Wei Ke, Sabine Süsstrunk, and Mathieu Salzmann. Leverage your local and global representations: A new self-supervised learning strategy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16580–16589, 2022c.
Xuyang Zhao, Tianqi Du, Yisen Wang, Jun Yao, and Weiran Huang. ArCL: Enhancing contrastive learning with augmentation-robust representations. arXiv preprint arXiv:2303.01092, 2023.
Mingkai Zheng, Shan You, Fei Wang, Chen Qian, Changshui Zhang, Xiaogang Wang, and Chang Xu. ReSSL: Relational self-supervised learning with weak augmentation. Advances in Neural Information Processing Systems, 34:2543–2555, 2021.
Yuanyi Zhong, Haoran Tang, Junkun Chen, Jian Peng, and Yu-Xiong Wang. Is self-supervised contrastive learning more robust than supervised learning? In First Workshop on Pre-training: Perspectives, Pitfalls, and Paths Forward at ICML 2022, 2022.
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong. iBOT: Image BERT pre-training with online tokenizer. arXiv preprint arXiv:2111.07832, 2021.
Pan Zhou, Yichen Zhou, Chenyang Si, Weihao Yu, Teck Khim Ng, and Shuicheng Yan. Mugs: A multigranular self-supervised learning framework. arXiv preprint arXiv:2203.14415, 2022.
Chengxu Zhuang, Alex Lin Zhai, and Daniel Yamins. Local aggregation for unsupervised learning of visual embeddings. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6002– 6012, 2019.
Andrew Zisserman. Self-supervised learning. 2018. URL https://project.inria.fr/paiss/files/2018/07/zisserman-self-supervised.pdf.