A Direct Approach to Robust Deep Learning Using Adversarial Networks

23 May 2019

Papers citing "A Direct Approach to Robust Deep Learning Using Adversarial Networks"

10 / 10 papers shown

Title
DTA: Distribution Transform-based Attack for Query-Limited Scenario Renyang Liu Wei Zhou Xin Jin Song Gao Yuanyu Wang Ruxin Wang 21 0 0 12 Dec 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min Lin Weiwei Liu Shuicheng Yan DiffM 28 210 0 09 Feb 2023
Advances in adversarial attacks and defenses in computer vision: A survey Naveed Akhtar Ajmal Mian Navid Kardan M. Shah AAML 41 236 0 01 Aug 2021
A Robust Adversarial Network-Based End-to-End Communications System With Strong Generalization Ability Against Adversarial Attacks Yudi Dong Huaxia Wang Yu-dong Yao AAML GAN 24 5 0 03 Mar 2021
AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows H. M. Dolatabadi S. Erfani C. Leckie AAML 19 66 0 15 Jul 2020
Increasing-Margin Adversarial (IMA) Training to Improve Adversarial Robustness of Neural Networks Linhai Ma Liang Liang AAML 26 18 0 19 May 2020
Data-Free Network Quantization With Adversarial Knowledge Distillation Yoojin Choi Jihwan P. Choi Mostafa El-Khamy Jungwon Lee MQ 27 119 0 08 May 2020
Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks Jianhe Yuan Zhihai He AAML 32 22 0 23 Apr 2020
Improving Robustness of Deep-Learning-Based Image Reconstruction Ankit Raj Y. Bresler Bo-wen Li OOD AAML 29 50 0 26 Feb 2020
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 303 3,115 0 04 Nov 2016