Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness

4 October 2018

Papers citing "Feature Prioritization and Regularization Improve Standard Accuracy and Adversarial Robustness"

10 / 10 papers shown

Title
Practical No-box Adversarial Attacks with Training-free Hybrid Image Transformation Qilong Zhang Chaoning Zhang Chaoning Zhang Chaoqun Li Xuanhan Wang Jingkuan Song Lianli Gao AAML 89 21 0 09 Mar 2022
Improving the Generalization of Adversarial Training with Domain Adaptation Chuanbiao Song Kun He Liwei Wang John E. Hopcroft AAML OOD 94 131 0 01 Oct 2018
Robustness May Be at Odds with Accuracy Dimitris Tsipras Shibani Santurkar Logan Engstrom Alexander Turner Aleksander Madry AAML 93 1,776 0 30 May 2018
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models Pouya Samangouei Maya Kabkab Rama Chellappa AAML GAN 82 1,176 0 17 May 2018
Learn To Pay Attention Saumya Jetley Nicholas A. Lord Namhoon Lee Philip Torr 93 439 0 06 Apr 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 197 3,180 0 01 Feb 2018
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser Fangzhou Liao Ming Liang Yinpeng Dong Tianyu Pang Xiaolin Hu Jun Zhu 83 883 0 08 Dec 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 279 12,029 0 19 Jun 2017
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 461 3,138 0 04 Nov 2016
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention Ke Xu Jimmy Ba Ryan Kiros Kyunghyun Cho Aaron Courville Ruslan Salakhutdinov R. Zemel Yoshua Bengio DiffM 324 10,050 0 10 Feb 2015