Fetching the paper…
Reading the bibliography…
Adversarial training (AT) has become a popular choice for training robust networks.
Instance Adaptive Adversarial Training: Improved Accuracy Tradeoffs in Neural Nets
Yogesh Balaji, Tom Goldstein, and Judy Hoffman. 2019 · 1910
Earlier work this paper cites.
Second-Order Derivatives of Extremal-Value Functions and Optimality Conditions for Semi-Infinite Programs
Alexander Shapiro. 1985 · 1985
Earlier work this paper cites.
Automatic Learning Rate Maximization by On-Line Estimation of the Hessian’s Eigenvectors. In Proceedings of the 5th International Conference on Neural Information Processing Systems (NIPS’92) . Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 156–163
Yann LeCun, Patrice Y. Simard, and Barak Pearlmutter. 1992 · 1992
Earlier work this paper cites.
Flat Minima
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
CAT: Customized Adversarial Training for Improved Robustness
Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, and Cho-Jui Hsieh. 2020 · 2002
Earlier work this paper cites.
Curriculum Learning. In Proceedings of the 26th Annual International Conference on Machine Learning - ICML ’09 . ACM Press, Montreal, Quebec, Canada, 1–8
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston. 2009 · 2009
Earlier work this paper cites.
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples
Sven Gowal, Chongli Qin, Jonathan Uesato, Timothy Mann, and Pushmeet Kohli. 2021 · 2010
Earlier work this paper cites.
Evasion Attacks against Machine Learning at Test Time. In Machine Learning and Knowledge Discovery in Databases , Hendrik Blockeel, Kristian Kersting, Siegfried Nijssen, and Filip Železný (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 387–402
Battista Biggio, Igino Corona, Davide Maiorca, Blaine Nelson, Nedim Šrndić, Pavel Laskov, Giorgio Giacinto, and Fabio Roli. 2013 · 2013
Earlier work this paper cites.
Intriguing Properties of Neural Networks. In International Conference on Learning Representations
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014 · 2014
Earlier work this paper cites.
Explaining and Harnessing Adversarial Examples. In International Conference on Learning Representations
Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015 · 2015
Earlier work this paper cites.
Gradient Descent Only Converges to Minimizers. In 29th Annual Conference on Learning Theory (Proceedings of Machine Learning Research, Vol. 49) , Vitaly Feldman, Alexander Rakhlin, and Ohad Shamir (Eds.). PMLR, Columbia University, New York, New York, USA, 1246–1257
Jason D. Lee, Max Simchowitz, Michael I. Jordan, and Benjamin Recht. 2016 · 2016
Earlier work this paper cites.
Theoretically-Grounded Policy Advice from Multiple Teachers in Reinforcement Learning Settings with Applications to Negative Transfer
Yusen Zhan, Haitham Bou Ammar, and Matthew E. Taylor. 2016 · 2016
Earlier work this paper cites.
Entropy-SGD: Biasing Gradient Descent into Wide Valleys
Pratik Chaudhari, Anna Choromanska, Stefano Soatto, Yann LeCun, Carlo Baldassi, Christian Borgs, Jennifer Chayes, Levent Sagun, and Riccardo Zecchina. 2017 · 2017
Earlier work this paper cites.
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Nitish Shirish Keskar, Dheevatsa Mudigere, Jorge Nocedal, Mikhail Smelyanskiy, and Ping Tak Peter Tang. 2017 · 2017
Cited alongside, same era.
Residual Attention Network for Image Classification
Fei Wang, Mengqing Jiang, Chen Qian, Shuo Yang, Cheng Li, Honggang Zhang, Xiaogang Wang, and Xiaoou Tang. 2017 · 2017
Cited alongside, same era.
Sergey Zagoruyko and Nikos Komodakis. 2017 · 2017
Cited alongside, same era.
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80) , Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholmsmässan, Stockholm Sweden, 274–283
Anish Athalye, Nicholas Carlini, and David Wagner. 2018 · 2018
On the Convergence and Robustness of Adversarial Training. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, Long Beach, California, USA, 6586–6595
Yisen Wang, Xingjun Ma, James Bailey, Jinfeng Yi, Bowen Zhou, and Quanquan Gu. 2019 · 2019
Later among the works it cites.
Theoretically Principled Trade-off between Robustness and Accuracy. In International Conference on Machine Learning
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan. 2019 · 2019
Later among the works it cites.
Reliable Evaluation of Adversarial Robustness with an Ensemble of Diverse Parameter-Free Attacks. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 2206–2216
Francesco Croce and Matthias Hein. 2020 · 2020
Closest in time.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training. In International Conference on Learning Representations
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Curriculum Adversarial Training. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI-18 . International Joint Conferences on Artificial Intelligence Organization, 3740–3747
Qi-Zhi Cai, Chang Liu, and Dawn Song. 2018 · 2018
Cited alongside, same era.
Averaging weights leads to wider optima and better generalization. In 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018 (34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018) , Ricardo Silva, Amir Globerson, and Amir Globerson (Eds.). Association For Uncertainty in Artificial Intelligence (AUAI), 876–885
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson. 2018 · 2018
Cited alongside, same era.
Three Factors Influencing Minima in SGD
Stanisław Jastrzębski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, and Amos Storkey. 2018 · 2018
Cited alongside, same era.
Visualizing the Loss Landscape of Neural Nets
Hao Li, Zheng Xu, Gavin Taylor, Christoph Studer, and Tom Goldstein. 2018 · 2018
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks. In International Conference on Learning Representations
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2018 · 2018
Cited alongside, same era.
Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks
Daphna Weinshall, Gad Cohen, and Dan Amir. 2018 · 2018
Cited alongside, same era.
On the Power of Curriculum Learning in Training Deep Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97) , Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 2535–2544
Guy Hacohen and Daphna Weinshall. 2019 · 2019
Cited alongside, same era.
Convergence Guarantees for a Class of Non-Convex and Non-Smooth Optimization Problems
Koulik Khamaru and Martin J. Wainwright. 2019 · 2019
Cited alongside, same era.
Gavin Weiguang Ding, Yash Sharma, Kry Yik Chau Lui, and Ruitong Huang. 2020 · 2020
Closest in time.
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them. In Advances in Neural Information Processing Systems
Chen Liu, Mathieu Salzmann, Tao Lin, Ryota Tomioka, and Sabine Süsstrunk. 2020 · 2020
Closest in time.
Unique Properties of Flat Minima in Deep Networks. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 7108–7118
Rotem Mulayoff and Tomer Michaeli. 2020 · 2020
Closest in time.
Overfitting in Adversarially Robust Deep Learning. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 8093–8104
Leslie Rice, Eric Wong, and Zico Kolter. 2020 · 2020
Closest in time.
Improving Adversarial Robustness Requires Revisiting Misclassified Examples. In International Conference on Learning Representations
Yisen Wang, Difan Zou, Jinfeng Yi, James Bailey, Xingjun Ma, and Quanquan Gu. 2020 · 2020
Closest in time.
Fast Is Better than Free: Revisiting Adversarial Training. In International Conference on Learning Representations
Eric Wong, Leslie Rice, and J. Zico Kolter. 2020 · 2020
Closest in time.
Adversarial Weight Perturbation Helps Robust Generalization. In Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 2958–2969
Dongxian Wu, Shu-Tao Xia, and Yisen Wang. 2020 · 2020
Closest in time.
Jingfeng Zhang, Xilie Xu, Bo Han, Gang Niu, Lizhen Cui, Masashi Sugiyama, and Mohan Kankanhalli. 2020 · 2020
Closest in time.
Fastai/Imagenette
Jeremy Howard. 2021 · 2021
Closest in time.