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Convolutional Neural Networks (CNNs) have made significant progress on several computer vision benchmarks, but are fraught with numerous non-human biases such as vulnerability to adversarial samples.
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
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Exploiting linear structure within convolutional networks for efficient evaluation
Emily L Denton, Wojciech Zaremba, Joan Bruna, Yann LeCun, and Rob Fergus · 2014
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, Klaus-Robert Müller, and Wojciech Samek · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deepfool: a simple and accurate method to fool deep neural networks
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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You only look once: Unified, real-time object detection
Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi · 2016
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" why should i trust you?" explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods
Nicholas Carlini and David Wagner · 2017
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Mask r-cnn
Kaiming He, Georgia Gkioxari, Piotr Dollár, and Ross Girshick · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
Jason Jo and Yoshua Bengio · 2017
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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Universal adversarial perturbations
S. Moosavi-Dezfooli, A. Fawzi, O. Fawzi, and P. Frossard · 2017
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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 · 2017
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Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Provable defenses against adversarial examples via the convex outer adversarial polytope
Eric Wong and J Zico Kolter · 2017
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Enhancing robustness of machine learning systems via data transformations
Adversarial examples are not bugs, they are features
Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, and Aleksander Madry · 2019
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Principal component properties of adversarial samples
Malhar Jere, Sandro Herbig, Christine Lind, and Farinaz Koushanfar · 2019
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Scratch that! an evolution-based adversarial attack against neural networks
Malhar Jere, Briland Hitaj, Gabriela Ciocarlie, and Farinaz Koushanfar · 2019
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Theory of the frequency principle for general deep neural networks
Tao Luo, Zheng Ma, Zhi-Qin John Xu, and Yaoyu Zhang · 2019
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Mohammad Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S Davis, Gavin Taylor, and Tom Goldstein · 2019
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Arjun Nitin Bhagoji, Daniel Cullina, Chawin Sitawarin, and Prateek Mittal · 2018
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Semantic adversarial examples
Hossein Hosseini and Radha Poovendran · 2018
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Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Trojaning attack on neural networks
Yingqi Liu, Shiqing Ma, Yousra Aafer, Wen-Chuan Lee, Juan Zhai, Weihang Wang, and Xiangyu Zhang · 2018
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
Cited alongside, same era.
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Learning robust representations by projecting superficial statistics out
Haohan Wang, Zexue He, Zachary C. Lipton, and Eric P. Xing · 2019
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Feature denoising for improving adversarial robustness
Cihang Xie, Yuxin Wu, Laurens van der Maaten, Alan L Yuille, and Kaiming He · 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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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P Xing, Laurent El Ghaoui, and Michael I Jordan · 2019
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Rethinking attention with performers
Krzysztof Choromanski, Valerii Likhosherstov, David Dohan, Xingyou Song, Andreea Gane, Tamas Sarlos, Peter Hawkins, Jared Davis, Afroz Mohiuddin, Lukasz Kaiser, et al · 2020
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Differences between human and machine perception in medical diagnosis
Taro Makino, Stanisław Jastrzębski, Witold Oleszkiewicz, Celin Chacko, Robin Ehrenpreis, Naziya Samreen, Chloe Chhor, Eric Kim, Jiyon Lee, Kristine Pysarenko, Beatriu Reig, Hildegard Toth, Divya Awal, Linda Du, Alice Kim, James Park, Daniel K. Sodickson, Laura Heacock, Linda Moy, Kyunghyun Cho, and Krzysztof J. Geras · 2020
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Can we learn heuristics for graphical model inference using reinforcement learning?
Safa Messaoud, Maghav Kumar, and Alexander G Schwing · 2020
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Adversarial deepfakes: Evaluating vulnerability of deepfake detectors to adversarial examples
Paarth Neekhara, Shehzeen Hussain, Malhar Jere, Farinaz Koushanfar, and Julian McAuley · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 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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Linformer: Self-attention with linear complexity
Sinong Wang, Belinda Li, Madian Khabsa, Han Fang, and Hao Ma · 2020
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Towards frequency-based explanation for robust cnn
Zifan Wang, Yilin Yang, Ankit Shrivastava, Varun Rawal, and Zihao Ding · 2020
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Attribution in scale and space
Shawn Xu, Subhashini Venugopalan, and Mukund Sundararajan · 2020
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