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Frequency analysis is useful for understanding the mechanisms of representation learning in neural networks (NNs).
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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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek · 2015
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Deep residual learning for image recognition, 2015
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Measuring the tendency of cnns to learn surface statistical regularities, 2017
Jason Jo and Yoshua Bengio · 2017
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness, 2018
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2018
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Analysis of explainers of black box deep neural networks for computer vision: A survey, 2019
Vanessa Buhrmester, David Münch, and Michael Arens · 2019
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Autoaugment: Learning augmentation strategies from data
Ekin D. Cubuk, Barret Zoph, Dandelion Mane, Vijay Vasudevan, and Quoc V. Le · 2019
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Imagenet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A. Wichmann, and Wieland Brendel · 2019
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Unmasking Clever Hans predictors and assessing what machines really learn
S. Lapuschkin, S. Wäldchen, A. Binder, et al · 2019
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On the spectral bias of neural networks
Nasim Rahaman, Aristide Baratin, Devansh Arpit, Felix Draxler, Min Lin, Fred Hamprecht, Yoshua Bengio, and Aaron Courville · 2019
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Grad-CAM: Visual explanations from deep networks via gradient-based localization
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2019
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A fourier perspective on model robustness in computer vision
Dong Yin, Raphael Gontijo Lopes, Jon Shlens, Ekin Dogus Cubuk, and Justin Gilmer · 2019
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Shortcut learning in deep neural networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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Augmix: A simple method to improve robustness and uncertainty under data shift
Dan Hendrycks*, Norman Mu*, Ekin Dogus Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan · 2020
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Frequency principle: Fourier analysis sheds light on deep neural networks
Zhi-Qin John Xu, Yaoyu Zhang, Tao Luo, Yanyang Xiao, and Zheng Ma · 2020
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A too-good-to-be-true prior to reduce shortcut reliance, 2021
Nikolay Dagaev, Brett D. Roads, Xiaoliang Luo, Daniel N. Barry, Kaustubh R. Patil, and Bradley C. Love · 2021
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Understanding layer-wise contributions in deep neural networks through spectral analysis, 2021
Yatin Dandi and Arthur Jacot · 2021
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Towards interpreting and mitigating shortcut learning behavior of nlu models, 2021
Mengnan Du, Varun Manjunatha, Rajiv Jain, Ruchi Deshpande, Franck Dernoncourt, Jiuxiang Gu, Tong Sun, and Xia Hu · 2021
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The many faces of robustness: A critical analysis of out-of-distribution generalization
D. Hendrycks, S. Basart, N. Mu, S. Kadavath, F. Wang, E. Dorundo, R. Desai, T. Zhu, S. Parajuli, M. Guo, D. Song, J. Steinhardt, and J. Gilmer · 2021
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Unlearnable examples: Making personal data unexploitable
Hanxun Huang, Xingjun Ma, Sarah Monazam Erfani, James Bailey, and Yisen Wang · 2021
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Automatic shortcut removal for self-supervised representation learning, 2020
Matthias Minderer, Olivier Bachem, Neil Houlsby, and Michael Tschannen · 2020
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The pitfalls of simplicity bias in neural networks
Harshay Shah, Kaustav Tamuly, Aditi Raghunathan, Prateek Jain, and Praneeth Netrapalli · 2020
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High-frequency component helps explain the generalization of convolutional neural networks
Haohan Wang, Xindi Wu, Zeyi Huang, and Eric P. Xing · 2020
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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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Frequency principle: Fourier analysis sheds light on deep neural networks
Zhi-Qin John Xu · 2020
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Deep frequency principle towards understanding why deeper learning is faster, 2020
Zhi-Qin John Xu and Hanxu Zhou · 2020
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Dissecting the high-frequency bias in convolutional neural networks
Antonio A. Abello, Roberto Hirata, and Zhangyang Wang · 2021
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Gradient starvation: A learning proclivity in neural networks
Mohammad Pezeshki, Sékou-Oumar Kaba, Yoshua Bengio, Aaron Courville, Doina Precup, and Guillaume Lajoie · 2021
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Can contrastive learning avoid shortcut solutions?, 2021
Joshua Robinson, Li Sun, Ke Yu, Kayhan Batmanghelich, Stefanie Jegelka, and Suvrit Sra · 2021
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A survey on explainable artificial intelligence (XAI): Toward medical XAI
Erico Tjoa and Cuntai Guan · 2021
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Uncovering and correcting shortcut learning in machine learning models for skin cancer diagnosis
Meike Nauta, Ricky Walsh, Adam Dubowski, and Christin Seifert · 2022
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An impartial take to the cnn vs transformer robustness contest
Francesco Pinto, Philip H. S. Torr, and Puneet K. Dokania · 2022
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Frequency shortcut learning in neural networks
Shunxin Wang, Raymond Veldhuis, Christoph Brune, and Nicola Strisciuglio · 2022
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DFM-X: Augmentation by leveraging prior knowledge of shortcut learning
Shunxin Wang, Christoph Brune, Raymond Veldhuis, and Nicola Strisciuglio · 2023
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Larger is not better: A survey on the robustness of computer vision models against common corruptions
Shunxin Wang, Raymond Veldhuis, Christoph Brune, and Nicola Strisciuglio · 2023
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