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For more than a decade, researchers have measured progress in object recognition on ImageNet-based generalization benchmarks such as ImageNet-A, -C, and -R.
Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas G. Dietterich · 1903
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Imagenet large scale visual recognition challenge, 2015
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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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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Does object recognition work for everyone?, 2019
Terrance DeVries, Ishan Misra, Changhan Wang, and Laurens van der Maaten · 2019
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Auditing imagenet: Towards a model-driven framework for annotating demographic attributes of large-scale image datasets, 2019
Chris Dulhanty and Alexander Wong · 2019
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Augmix: A simple data processing method to improve robustness and uncertainty
Dan Hendrycks, Norman Mu, Ekin D Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Pytorch image models
Ross Wightman · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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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, et al · 2020
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Generalisation in humans and deep neural networks, 2020
Robert Geirhos, Carlos R. Medina Temme, Jonas Rauber, Heiko H. Schütt, Matthias Bethge, and Felix A. Wichmann · 2020
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spaCy: Industrial-strength Natural Language Processing in Python
Matthew Honnibal, Ines Montani, Sofie Van Landeghem, and Adriane Boyd · 2020
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When and how do 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 · 2020
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Measuring robustness to natural distribution shifts in image classification, 2020
Rohan Taori, Achal Dave, Vaishaal Shankar, Nicholas Carlini, Benjamin Recht, and Ludwig Schmidt · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
Cited alongside, same era.
Noise or signal: The role of image backgrounds in object recognition, 2020
Kai Xiao, Logan Engstrom, Andrew Ilyas, and Aleksander Madry · 2020
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Exploring the limits of large scale pre-training, 2021
Samira Abnar, Mostafa Dehghani, Behnam Neyshabur, and Hanie Sedghi · 2021
Cited alongside, same era.
Large image datasets: A pyrrhic win for computer vision?
Abeba Birhane and Vinay Uday Prabhu · 2021
Cited alongside, same era.
Ai for radiographic covid-19 detection selects shortcuts over signal
Alex J DeGrave, Joseph D Janizek, and Su-In Lee · 2021
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The robustness limits of sota vision models to natural variation, 2022
Mark Ibrahim, Quentin Garrido, Ari Morcos, and Diane Bouchacourt · 2022
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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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Last layer re-training is sufficient for robustness to spurious correlations, 2022
Polina Kirichenko, Pavel Izmailov, and Andrew Gordon Wilson · 2022
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Explicit tradeoffs between adversarial and natural distributional robustness
Mazda Moayeri, Kiarash Banihashem, and Soheil Feizi · 2022
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Contrastive language-image pre-training with knowledge graphs, 2022
Xuran Pan, Tianzhu Ye, Dongchen Han, Shiji Song, and Gao Huang · 2022
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Abhimanyu Dubey, Vignesh Ramanathan, Alex Pentland, and Dhruv Mahajan · 2021
Cited alongside, same era.
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, et al · 2021
Cited alongside, same era.
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
Cited alongside, same era.
Can we have it all? on the trade-off between spatial and adversarial robustness of neural networks
Sandesh Kamath, Amit Deshpande, Subrahmanyam Kambhampati Venkata, and Vineeth N Balasubramanian · 2021
Cited alongside, same era.
Small in-distribution changes in 3d perspective and lighting fool both cnns and transformers
Spandan Madan, Tomotake Sasaki, Tzu-Mao Li, Xavier Boix, and Hanspeter Pfister · 2021
Cited alongside, same era.
Accuracy on the line: on the strong correlation between out-of-distribution and in-distribution generalization
John P Miller, Rohan Taori, Aditi Raghunathan, Shiori Sagawa, Pang Wei Koh, Vaishaal Shankar, Percy Liang, Yair Carmon, and Ludwig Schmidt · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision, 2021
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
Cited alongside, same era.
How robust is unsupervised representation learning to distribution shift?, 2022
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, and Amartya Sanyal · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos · 2022
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Id and ood performance are sometimes inversely correlated on real-world datasets
Damien Teney, Yong Lin, Seong Joon Oh, and Ehsan Abbasnejad · 2022
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Does progress on imagenet transfer to real-world datasets?, 2023
Alex Fang, Simon Kornblith, and Ludwig Schmidt · 2023
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Pinpointing why object recognition performance degrades across income levels and geographies, 2023
Laura Gustafson, Megan Richards, Melissa Hall, Caner Hazirbas, Diane Bouchacourt, and Mark Ibrahim · 2023
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Adversarial examples within the training distribution: A widespread challenge, 2023
Spandan Madan, Tomotake Sasaki, Hanspeter Pfister, Tzu-Mao Li, and Xavier Boix · 2023
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Quality not quantity: On the interaction between dataset design and robustness of clip, 2023
Thao Nguyen, Gabriel Ilharco, Mitchell Wortsman, Sewoong Oh, and Ludwig Schmidt · 2023
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Regmixup: Mixup as a regularizer can surprisingly improve accuracy and out distribution robustness, 2023
Francesco Pinto, Harry Yang, Ser-Nam Lim, Philip H. S. Torr, and Puneet K. Dokania · 2023
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Beyond web-scraping: Crowd-sourcing a geographically diverse image dataset
Vikram V Ramaswamy, Sing Yu Lin, Dora Zhao, Aaron B Adcock, Laurens van der Maaten, Deepti Ghadiyaram, and Olga Russakovsky · 2023
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Effective robustness against natural distribution shifts for models with different training data, 2023
Zhouxing Shi, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel, and Yao Qin · 2023
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