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Contrastive Learning (CL) has emerged as one of the most successful paradigms for unsupervised visual representation learning, yet it often depends on intensive manual data augmentations.
Spectral graph theory , volume 92
Fan RK Chung · 1997
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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The spectral gap of a random subgraph of a graph
Fan Chung and Paul Horn · 2007
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Generative adversarial networks
Ian J Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Diederik P Kingma and Max Welling · 2014
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Flownet: Learning optical flow with convolutional networks
Alexey Dosovitskiy, Philipp Fischer, Eddy Ilg, Philip Häusser, Caner Hazirbas, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Cross-domain self-supervised multi-task feature learning using synthetic imagery
Zhongzheng Ren and Yong Jae Lee · 2018
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Learning from synthetic data: Addressing domain shift for semantic segmentation
Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain, Ser Nam Lim, and Rama Chellappa · 2018
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This dataset does not exist: Training models from generated images
Victor Besnier, Himalaya Jain, Andrei Bursuc, Matthieu Cord, and Patrick Pérez · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Bootstrap your own latent: a new approach to self-supervised learning
Jean-Bastien Grill, Florian Strub, Florent Altché, C. Tallec, Pierre H. Richemond, Elena Buchatskaya, C. Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, B. Piot, K. Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Training generative adversarial networks with limited data
Tero Karras, Miika Aittala, Janne Hellsten, Samuli Laine, Jaakko Lehtinen, and Timo Aila · 2020
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What makes for good views for contrastive learning?
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
Tongzhou Wang and Phillip Isola · 2020
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Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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Exploring simple siamese representation learning
Xinlei Chen and Kaiming He · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Provable guarantees for self-supervised deep learning with spectral contrastive loss
Pretrained diffusion models for unified human motion synthesis
Jianxin Ma, Shuai Bai, and Chang Zhou · 2022
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Understanding contrastive learning requires incorporating inductive biases
Nikunj Saunshi, Jordan Ash, Surbhi Goel, Dipendra Misra, Cyril Zhang, Sanjeev Arora, Sham Kakade, and Akshay Krishnamurthy · 2022
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Towards a unified theoretical understanding of non-contrastive learning via rank differential mechanism
Zhijian Zhuo, Yifei Wang, Jinwen Ma, and Yisen Wang · 2022
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Synthetic data from diffusion models improves imagenet classification
Shekoofeh Azizi, Simon Kornblith, Chitwan Saharia, Mohammad Norouzi, and David J. Fleet · 2023
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Rethinking weak supervision in helping contrastive learning
Jingyi Cui, Weiran Huang, Yifei Wang, and Yisen Wang · 2023
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Jeff Z HaoChen, Colin Wei, Adrien Gaidon, and Tengyu Ma · 2021
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Learning to see by looking at noise
Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, and Antonio Torralba · 2021
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Understanding self-supervised learning dynamics without contrastive pairs
Yuandong Tian, Xinlei Chen, and S. Ganguli · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and Stéphane Deny · 2021
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Label-efficient semantic segmentation with diffusion models
Dmitry Baranchuk, Andrey Voynov, Ivan Rubachev, Valentin Khrulkov, and Artem Babenko · 2022
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Deep generative modelling: A comparative review of vaes, gans, normalizing flows, energy-based and autoregressive models
Sam Bond-Taylor, Adam Leach, Yang Long, and Chris G. Willcocks · 2022
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solo-learn: A library of self-supervised methods for visual representation learning
Victor Guilherme Turrisi da Costa, Enrico Fini, Moin Nabi, Nicu Sebe, and Elisa Ricci · 2022
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On the duality between contrastive and non-contrastive self-supervised learning
Quentin Garrido, Yubei Chen, Adrien Bardes, Laurent Najman, and Yann LeCun · 2023
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Contranorm: A contrastive learning perspective on oversmoothing and beyond
Xiaojun Guo, Yifei Wang, Tianqi Du, and Yisen Wang · 2023
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A theoretical study of inductive biases in contrastive learning
Jeff Z HaoChen and Tengyu Ma · 2023
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Is synthetic data from generative models ready for image recognition?
Ruifei He, Shuyang Sun, Xin Yu, Chuhui Xue, Wenqing Zhang, Philip H. S. Torr, Song Bai, and Xiaojuan Qi · 2023
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Rethinking the effect of data augmentation in adversarial contrastive learning
Rundong Luo, Yifei Wang, and Yisen Wang · 2023
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Stablerep: Synthetic images from text-to-image models make strong visual representation learners
Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, and Dilip Krishnan · 2023
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A message passing perspective on learning dynamics of contrastive learning
Yifei Wang, Qi Zhang, Tianqi Du, Jiansheng Yang, Zhouchen Lin, and Yisen Wang · 2023
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Synthetic data can also teach: Synthesizing effective data for unsupervised visual representation learning
Yawen Wu, Zhepeng Wang, Dewen Zeng, Yiyu Shi, and Jingtong Hu · 2023
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Stable target field for reduced variance score estimation in diffusion models
Yilun Xu, Shangyuan Tong, and Tommi S. Jaakkola · 2023
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