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Neural networks are susceptible to small perturbations in the form of 2D rotations and shifts, image crops, and even changes in object colors.
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Recognition of objects in non-canonical views: A functional MRI study
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Robustness of classifiers: from adversarial to random noise
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Adversarial perturbations of deep neural networks
David Warde-Farley and Ian Goodfellow · 2016
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Tinghui Zhou, Shubham Tulsiani, Weilun Sun, Jitendra Malik, and Alexei A Efros · 2016
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Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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A rotation and a translation suffice: Fooling CNNs with simple transformations
Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, and Aleksander Madry · 2018
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Chaowei Xiao, Dawei Yang, Bo Li, Jia Deng, and Mingyan Liu · 2019
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Adversarial attacks beyond the image space
Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi-Keung Tang, and Alan L Yuille · 2019
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Making convolutional networks shift-invariant again
Richard Zhang · 2019
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Robust out-of-distribution detection via informative outlier mining
Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang, and Somesh Jha · 2020
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Towards verifying robustness of neural networks against a family of semantic perturbations
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Justin Gilmer, Luke Metz, Fartash Faghri, Samuel S Schoenholz, Maithra Raghu, Martin Wattenberg, and Ian Goodfellow · 2018
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Semantic adversarial examples
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Strike (with) a pose: Neural networks are easily fooled by strange poses of familiar objects
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Why do deep convolutional networks generalize so poorly to small image transformations?
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CMA-ES/pycma on Github
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Synsin: End-to-end view synthesis from a single image
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On multiview robustness of 3D adversarial attacks
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Novel view synthesis of dynamic scenes with globally coherent depths from a monocular camera
Jae Shin Yoon, Kihwan Kim, Orazio Gallo, Hyun Soo Park, and Jan Kautz · 2020
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Truly shift-invariant convolutional neural networks
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