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Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classification.
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 1907
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2001
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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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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 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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Dataset shift in machine learning
Joaquin Quinonero-Candela, Masashi Sugiyama, Anton Schwaighofer, and Neil D. Lawrence · 2009
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2010
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Representation learning: A review and new perspectives
Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2013
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beta-VAE: Learning basic visual concepts with a constrained variational framework
Irina Higgins, Loic Matthey, Arka Pal, Christopher Burgess, Xavier Glorot, Matthew Botvinick, Shakir Mohamed, and Alexander Lerchner · 2017
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spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing
Matthew Honnibal and Ines Montani · 2017
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Disentangling by factorising
Hyunjik Kim and Andriy Mnih · 2018
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An intriguing failing of convolutional neural networks and the coordconv solution
Rosanne Liu, Joel Lehman, Piero Molino, Felipe Petroski Such, Eric Frank, Alex Sergeev, and Jason Yosinski · 2018
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Pooling is neither necessary nor sufficient for appropriate deformation stability in cnns, 2018
Avraham Ruderman, Neil C. Rabinowitz, Ari S. Morcos, and Daniel Zoran · 2018
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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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Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
Andrei Barbu, David Mayo, Julian Alverio, William Luo, Christopher Wang, Dan Gutfreund, Josh Tenenbaum, and Boris Katz · 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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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Muhammad Waleed Gondal, Manuel Wuthrich, Djordje Miladinovic, Francesco Locatello, Martin Breidt, Valentin Volchkov, Joel Akpo, Olivier Bachem, Bernhard Schölkopf, and Stefan Bauer · 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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Understanding image representations by measuring their equivariance and equivalence
Karel Lenc and Andrea Vedaldi · 2019
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Self-supervised pretraining of visual features in the wild
Priya Goyal, Mathilde Caron, Benjamin Lefaudeux, Min Xu, Pengchao Wang, Vivek Pai, Mannat Singh, Vitaliy Liptchinsky, Ishan Misra, Armand Joulin, and Piotr Bojanowski · 2021
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Openclip, July 2021
Gabriel Ilharco, Mitchell Wortsman, Ross Wightman, Cade Gordon, Nicholas Carlini, Rohan Taori, Achal Dave, Vaishaal Shankar, Hongseok Namkoong, John Miller, Hannaneh Hajishirzi, Ali Farhadi, and Ludwig Schmidt · 2021
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Small in-distribution changes in 3d perspective and lighting fool both cnns and transformers, 2021
Spandan Madan, Tomotake Sasaki, Tzu-Mao Li, Xavier Boix, and Hanspeter Pfister · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Imagenet-21k pretraining for the masses, 2021
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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Pytorch image models
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A framework for contrastive self-supervised learning and designing a new approach
William Falcon and Kyunghyun Cho · 2020
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On the surprising similarities between supervised and self-supervised models
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On translation invariance in cnns: Convolutional layers can exploit absolute spatial location
Osman Semih Kayhan and Jan C. van Gemert · 2020
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Convolutional neural networks are not invariant to translation, but they can learn to be
Valerio Biscione and Jeffrey S. Bowers · 2021
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High fidelity visualization of what your self-supervised representation knows about
Florian Bordes, Randall Balestriero, and Pascal Vincent · 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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Resnet strikes back: An improved training procedure in timm
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2021
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Progress and limitations of deep networks to recognize objects in unusual poses
Amro Abbas and Stéphane Deny · 2022
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Masked autoencoders are scalable vision learners
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