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How do humans learn to acquire a powerful, flexible and robust representation of objects? While much of this process remains unknown, it is clear that humans do not require millions of object labels.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2003
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A Wichmann · 2004
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Predictive learning, 2016
Yann LeCun · 2016
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Understanding how image quality affects deep neural networks
Samuel Dodge and Lina Karam · 2016
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos RM Temme, Jonas Rauber, Heiko H Schütt, Matthias Bethge, and Felix A Wichmann · 2018
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Unsupervised feature learning via non-parametric instance discrimination
Zhirong Wu, Yuanjun Xiong, Stella X Yu, and Dahua Lin · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Deep convolutional networks do not classify based on global object shape
Nicholas Baker, Hongjing Lu, Gennady Erlikhman, and Philip J Kellman · 2018
Cited alongside, same era.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
Cited alongside, same era.
PyTorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
Cited alongside, same era.
Approximating CNNs with bag-of-local-features models works surprisingly well on ImageNet
Wieland Brendel and Matthias Bethge · 2019
Cited alongside, same era.
Self-supervised learning through the eyes of a child
A Emin Orhan, Vaibhav V Gupta, and Brenden M Lake · 2020
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Instance-level contrastive learning yields human brain-like representation without category-supervision
Talia Konkle and George A Alvarez · 2020
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Unsupervised neural network models of the ventral visual stream
Chengxu Zhuang, Siming Yan, Aran Nayebi, Martin Schrimpf, Michael Frank, James DiCarlo, and Daniel Yamins · 2020
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Unsupervised learning predicts human perception and misperception of specular surface reflectance
Katherine R Storrs and Roland W Fleming · 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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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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A neural network trained for prediction mimics diverse features of biological neurons and perception
William Lotter, Gabriel Kreiman, and David Cox · 2020
Cited alongside, same era.
Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency
Robert Geirhos, Kristof Meding, and Felix A Wichmann
Cited in the paper.
Yonglong Tian, Chen Sun, Ben Poole, Dilip Krishnan, Cordelia Schmid, and Phillip Isola · 2020
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The origins and prevalence of texture bias in convolutional neural networks
Katherine L Hermann, Ting Chen, and Simon Kornblith · 2020
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Emergent properties of foveated perceptual systems
Arturo Deza and Talia Konkle · 2020
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