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Deep learning has led to remarkable advances in computer vision.
No representation without transformation
Giorgio Giannone, Jonathan Masci, and Christian Osendorfer · 1912
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2002
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Lie Groups, Lie Algebras, and Representations: An Elementary Introduction
B.C. Hall · 2003
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Variational autoencoder with learned latent structure
Marissa C. Connor, Gregory H. Canal, and Christopher J. Rozell · 2006
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When and how CNNs generalize to out-of-distribution category-viewpoint combinations
Spandan Madan, Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, Frédo Durand, Hanspeter Pfister, and Xavier Boix · 2007
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Naive Lie Theory
J. Stillwell · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning a lie algebra from unlabeled data pairs
Chris Ick and Vincent Lostanlen · 2009
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Mnist handwritten digit database
Yann LeCun, Corinna Cortes, and CJ Burges · 2010
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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ilab-20m: A large-scale controlled object dataset to investigate deep learning
Ali Borji, Saeed Izadi, and Laurent Itti · 2016
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio · 2016
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Unsupervised transformation learning via convex relaxations
Tatsunori B Hashimoto, Percy S Liang, and John C Duchi · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Dynamic routing between capsules
Sara Sabour, Nicholas Frosst, and Geoffrey E. Hinton · 2017
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Generalisation in humans and deep neural networks
Robert Geirhos, Carlos R. M. Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, and Felix A. Wichmann · 2018
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Towards a Definition of Disentangled Representations
Irina Higgins, David Amos, David Pfau, Sebastien Racaniere, Loic Matthey, Danilo Rezende, and Alexander Lerchner · 2018
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Representing closed transformation paths in encoded network latent space
Marissa Connor and Christopher Rozell · 2020
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Crosstransformers: spatially-aware few-shot transfer
Carl Doersch, Ankush Gupta, and Andrew Zisserman · 2020
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On the surprising similarities between supervised and self-supervised models
Robert Geirhos, Kantharaju Narayanappa, Benjamin Mitzkus, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2020
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Weakly-supervised disentanglement without compromises
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen · 2020
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Deep domain-adversarial image generation for domain generalisation
Kaiyang Zhou, Yongxin Yang, Timothy M. Hospedales, and Tao Xiang · 2020
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VICReg: Variance-invariance-covariance regularization for self-supervised learning
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
Michael A. Alcorn, Qi Li, Zhitao Gong, Chengfei Wang, Long Mai, Wei-Shinn Ku, and Anh Nguyen · 2019
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Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay and Yair Weiss · 2019
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Exploring the landscape of spatial robustness
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2019
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Equivariant Transformer Networks
Kai Sheng Tai, Peter Bailis, and Gregory Valiant · 2019
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Pytorch image models
Ross Wightman · 2019
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Adrien Bardes, Jean Ponce, and Yann LeCun · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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Efficient-capsnet: capsule network with self-attention routing
Vittorio Mazzia, Francesco Salvetti, and Marcello Chiaberge · 2021
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Domain invariant representation learning with domain density transformations
A. Tuan Nguyen, Toan Tran, Yarin Gal, and Atilim Gunes Baydin · 2021
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Model-based domain generalization
Alexander Robey, George J. Pappas, and Hamed Hassani · 2021
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Self-supervised learning with data augmentations provably isolates content from style
Julius von Kügelgen, Yash Sharma, Luigi Gresele, Wieland Brendel, Bernhard Schölkopf, Michel Besserve, and Francesco Locatello · 2021
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Contrastive learning inverts the data generating process
Roland S. Zimmermann, Yash Sharma, Steffen Schneider, Matthias Bethge, and Wieland Brendel · 2021
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Simple data balancing achieves competitive worst-group-accuracy
Badr Youbi Idrissi, Martin Arjovsky, Mohammad Pezeshki, and David Lopez-Paz · 2022
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3d warehouse
Trimble Inc · 2022
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