Variance Networks: When Expectation Does Not Meet Your Expectations
Authors: Kirill Neklyudov, Dmitry Molchanov, Arsenii Ashukha, Dmitry Vetrov | Published: 2018-03-10 | Updated: 2019-02-18
敵対的学習
Stochastic Activation Pruning for Robust Adversarial Defense
Authors: Guneet S. Dhillon, Kamyar Azizzadenesheli, Zachary C. Lipton, Jeremy Bernstein, Jean Kossaifi, Aran Khanna, Anima Anandkumar | Published: 2018-03-05
Understanding and Enhancing the Transferability of Adversarial Examples
Authors: Lei Wu, Zhanxing Zhu, Cheng Tai, Weinan E | Published: 2018-02-27
Adversarial Training for Probabilistic Spiking Neural Networks
Authors: Alireza Bagheri, Osvaldo Simeone, Bipin Rajendran | Published: 2018-02-22 | Updated: 2018-02-26
Are Generative Classifiers More Robust to Adversarial Attacks?
Authors: Yingzhen Li, John Bradshaw, Yash Sharma | Published: 2018-02-19 | Updated: 2019-05-27
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Authors: Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord, Pushmeet Kohli | Published: 2018-02-15 | Updated: 2018-06-12
Distributed One-class Learning
Authors: Ali Shahin Shamsabadi, Hamed Haddadi, Andrea Cavallaro | Published: 2018-02-10
Certified Robustness to Adversarial Examples with Differential Privacy
Authors: Mathias Lecuyer, Vaggelis Atlidakis, Roxana Geambasu, Daniel Hsu, Suman Jana | Published: 2018-02-09 | Updated: 2019-05-29
Blind Pre-Processing: A Robust Defense Method Against Adversarial Examples
Authors: Adnan Siraj Rakin, Zhezhi He, Boqing Gong, Deliang Fan | Published: 2018-02-05 | Updated: 2018-02-07
Sparsity-based Defense against Adversarial Attacks on Linear Classifiers
Authors: Zhinus Marzi, Soorya Gopalakrishnan, Upamanyu Madhow, Ramtin Pedarsani | Published: 2018-01-15 | Updated: 2018-06-19