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Unsupervised learning has recently made exceptional progress because of the development of more effective contrastive learning methods.
Pyramid-based texture analysis/synthesis
D. J. Heeger and J. R. Bergen · 1995
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Multiresolution sampling procedure for analysis and synthesis of texture images
J. S. De Bonet · 1997
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Texture synthesis by non-parametric sampling
A. A. Efros and T. K. Leung · 1999
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A parametric texture model based on joint statistics of complex wavelet coefficients
J. Portilla and E. P. Simoncelli · 2000
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Fast texture synthesis using tree-structured vector quantization
L.-Y. Wei and M. Levoy · 2000
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Synthesizing natural textures
M. Ashikhmin · 2001
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Discriminative learning of deep convolutional feature point descriptors
E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer · 2015
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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 2016
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T. B. Brown, D. Mané, A. Roy, M. Abadi, and J. Gilmer · 2017
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Smart mining for deep metric learning
B. Harwood, V. Kumar B G, G. Carneiro, I. Reid, and T. Drummond · 2017
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Adversarial examples for evaluating reading comprehension systems
R. Jia and P. Liang · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
J. Jo and Y. Bengio · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
J. Buolamwini and T. Gebru · 2018
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Generalisation in humans and deep neural networks
R. Geirhos, C. R. M. Temme, J. Rauber, H. H. Schütt, M. Bethge, and F. A. Wichmann · 2018
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Unsupervised hard example mining from videos for improved object detection
S. Jin, A. RoyChowdhury, H. Jiang, A. Singh, A. Prasad, D. Chakraborty, and E. Learned-Miller · 2018
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Examining gender and race bias in two hundred sentiment analysis systems
S. Kiritchenko and S. M. Mohammad · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Z. Wu, Y. Xiong, S. X. Yu, and D. Lin · 2018
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Approximating CNNs with bag-of-local-features models works surprisingly well on imagenet
W. Brendel and M. Bethge · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
D. Hendrycks and T. Dietterich · 2019
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The contrasting roles of shape in human vision and convolutional neural networks
G. Malhotra and J. Bowers · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
T. McCoy, E. Pavlick, and T. Linzen · 2019
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Learning robust global representations by penalizing local predictive power
H. Wang, S. Ge, Z. Lipton, and E. P. Xing · 2019
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Are we done with imagenet?, 2020
L. Beyer, O. J. Hénaff, A. Kolesnikov, X. Zhai, and A. van den Oord · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Adversarial self-supervised contrastive learning
M. Kim, J. Tack, and S. J. Hwang · 2020
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Learning visual representations for transfer learning by suppressing texture
S. Mishra, A. Shah, A. Bansal, J. Choi, A. Shrivastava, A. Sharma, and D. Jacobs · 2020
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Self-supervised learning of pretext-invariant representations
I. Misra and L. v. d. Maaten · 2020
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Measuring robustness to natural distribution shifts in image classification
R. Taori, A. Dave, V. Shankar, N. Carlini, B. Recht, and L. Schmidt · 2020
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Contrastive multiview coding
Y. Tian, D. Krishnan, and P. Isola · 2020
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M. Caron, I. Misra, J. Mairal, P. Goyal, P. Bojanowski, and A. Joulin · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
X. Chen, H. Fan, R. Girshick, and K. He · 2020
Cited alongside, same era.
Exploring simple siamese representation learning
X. Chen and K. He · 2020
Cited alongside, same era.
Debiased contrastive learning
C.-Y. Chuang, J. Robinson, Y.-C. Lin, A. Torralba, and S. Jegelka · 2020
Cited alongside, same era.
Are all negatives created equal in contrastive instance discrimination?
J. Frankle, D. J. Schwab, A. S. Morcos, et al · 2020
Cited alongside, same era.
Bootstrap your own latent - a new approach to self-supervised learning
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, B. Piot, k. kavukcuoglu, R. Munos, and M. Valko · 2020
Cited alongside, same era.
Y. Tian, C. Sun, B. Poole, D. Krishnan, C. Schmid, and P. Isola · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
H. Wang, X. Wu, Z. Huang, and E. P. Xing · 2020
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What should not be contrastive in contrastive learning
T. Xiao, X. Wang, A. A. Efros, and T. Darrell · 2020
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Hard negative examples are hard, but useful
H. Xuan, A. Stylianou, X. Liu, and R. Pless · 2020
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Unlearnable examples: Making personal data unexploitable
H. Huang, X. Ma, S. M. Erfani, J. Bailey, and Y. Wang · 2021
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Shape or texture: Understanding discriminative features in {cnn}s
M. A. Islam, M. Kowal, P. Esser, S. Jia, B. Ommer, K. G. Derpanis, and N. Bruce · 2021
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Cutpaste: Self-supervised learning for anomaly detection and localization
C.-L. Li, K. Sohn, J. Yoon, and T. Pfister · 2021
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Shape-texture debiased neural network training
Y. Li, Q. Yu, M. Tan, J. Mei, P. Tang, W. Shen, A. Yuille, and cihang xie · 2021
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Does enhanced shape bias improve neural network robustness to common corruptions?
C. K. Mummadi, R. Subramaniam, R. Hutmacher, J. Vitay, V. Fischer, and J. H. Metzen · 2021
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Contrastive learning with hard negative samples
J. Robinson, C.-Y. Chuang, S. Sra, and S. Jegelka · 2021
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Can contrastive learning avoid shortcut solutions?
J. Robinson, L. Sun, K. Yu, K. Batmanghelich, S. Jegelka, and S. Sra · 2021
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C. K. Ryali, D. J. Schwab, and A. S. Morcos · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
J. Zbontar, L. Jing, I. Misra, Y. LeCun, and S. Deny · 2021
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