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For an image query, unsupervised contrastive learning labels crops of the same image as positives, and other image crops as negatives.
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Diverse neural network learns true target functions
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The expressive power of neural networks: A view from the width
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Empirical analysis of the hessian of over-parametrized neural networks
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Learning deep representations by mutual information estimation and maximization
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Detectron2, 2019
Y. Wu, A. Kirillov, F. Massa, W. Lo, and R. Girshick · 2019
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X. Chen, H. Fan, R. Girshick, and K. He · 2020
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Big self-supervised models are strong semi-supervised learners
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A simple framework for contrastive learning of visual representations
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Unsupervised learning of visual features by contrasting cluster assignments
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Label refinery: Improving imagenet classification through label progression
H. Bagherinezhad, M. Horton, M. Rastegari, and A. Farhadi · 2018
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Learning overparameterized neural networks via stochastic gradient descent on structured data
Y. Li and Y. Liang · 2018
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Gradient descent provably optimizes over-parameterized neural networks
S. Du, X. Zhai, B. Poczos, and A. Singh · 2018
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Deep learning for healthcare: review, opportunities and challenges
R. Miotto, F. Wang, S. Wang, X. Jiang, and J. Dudley · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. Yu, and D. Lin · 2018
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Learning representations by maximizing mutual information across views
P. Bachman, R. Hjelm, and W. Buchwalter · 2019
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A theoretical analysis of contrastive unsupervised representation learning
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M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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Bootstrap your own latent: A new approach to self-supervised learning
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C. Chuang, J. Robinson, Y. Lin, A. Torralba, and S. Jegelka · 2020
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Co2: Consistent contrast for unsupervised visual representation learning
C. Wei, H. Wang, W. Shen, and A. Yuille · 2020
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Mixco: Mix-up contrastive learning for visual representation
S. Kim, G. Lee, S. Bae, and S. Yun · 2020
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i-mix: A strategy for regularizing contrastive representation learning
K. Lee, Y. Zhu, K. Sohn, C. Li, J. Shin, and H. Lee · 2020
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Towards domain-agnostic contrastive learning
V. Verma, M. Luong, K. Kawaguchi, H. Pham, and Q. Le · 2020
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Gradient descent with early stopping is provably robust to label noise for overparameterized neural networks
M. Li, M. Soltanolkotabi, and S. Oymak · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. Maaten · 2020
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Exploring simple siamese representation learning
X. Chen and K. He · 2020
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What makes for good views for contrastive learning
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
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Learning representations by predicting bags of visual words
S. Gidaris, A. Bursuc, N. Komodakis, P. Pérez, and M. Cord · 2020
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S. Oymak and M. Soltanolkotabi · 2020
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Contrastive learning with stronger augmentations
X. Wang and G. Qi · 2021
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