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Deep networks should be robust to rare events if they are to be successfully deployed in high-stakes real-world applications (e.g., self-driving cars).
Mental rotation of three-dimensional objects
Roger N. Shepard and Jacqueline Metzler · 1971
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The time to name disoriented natural objects
Pierre Jolicoeur · 1985
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Distilling the knowledge in a neural network
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Imagenet large scale visual recognition challenge
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Deep residual learning for image recognition
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Rethinking the inception architecture for computer vision
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A study and comparison of human and deep learning recognition performance under visual distortions
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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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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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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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2019
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Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior
Kohitij Kar, Jonas Kubilius, Kailyn Schmidt, Elias B. Issa, and James J. DiCarlo · 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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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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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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Sharpness-aware minimization for efficiently improving generalization
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Big transfer (bit): General visual representation learning
Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Joan Puigcerver, Jessica Yung, Sylvain Gelly, and Neil Houlsby · 2020
Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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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
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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
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Meta pseudo labels
Hieu Pham, Zihang Dai, Qizhe Xie, and Quoc V Le · 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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Measuring robustness to natural distribution shifts in image classification
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Self-training with noisy student improves imagenet classification
Qizhe Xie, Minh-Thang Luong, Eduard Hovy, and Quoc V Le · 2020
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Beit: Bert pre-training of image transformers
Hangbo Bao, Li Dong, and Furu Wei · 2021
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When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception
Tyler Bonnen, Daniel L. K. Yamins, and Anthony D. Wagner · 2021
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When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2021
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Convit: Improving vision transformers with soft convolutional inductive biases
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Partial success in closing the gap between human and machine vision
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Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction
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Do image classifiers generalize across time?
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Mlp-mixer: An all-mlp architecture for vision
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Training data-efficient image transformers & distillation through attention
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Florence: A new foundation model for computer vision
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Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, and Saining Xie · 2022
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Revisiting weakly supervised pre-training of visual perception models
Mannat Singh, Laura Gustafson, Aaron Adcock, Vinicius de Freitas Reis, Bugra Gedik, Raj Prateek Kosaraju, Dhruv Mahajan, Ross Girshick, Piotr Dollár, and Laurens van der Maaten · 2022
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