Improving Adversarial Robustness via Promoting Ensemble Diversity

25 January 2019

Kun Xu

Jun Zhu

Papers citing "Improving Adversarial Robustness via Promoting Ensemble Diversity"

26 / 76 papers shown

Title
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning Ren Wang Kaidi Xu Sijia Liu Pin-Yu Chen Tsui-Wei Weng Chuang Gan Meng Wang AAML 21 46 0 20 Feb 2021
Guided Interpolation for Adversarial Training Chen Chen Jingfeng Zhang Xilie Xu Tianlei Hu Gang Niu Gang Chen Masashi Sugiyama AAML 30 10 0 15 Feb 2021
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models Sahar Abdelnabi Mario Fritz AAML 24 7 0 09 Feb 2021
DICE: Diversity in Deep Ensembles via Conditional Redundancy Adversarial Estimation Alexandre Ramé Matthieu Cord FedML 50 51 0 14 Jan 2021
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints X. Li Xiangrui Li Deng Pan D. Zhu AAML 13 17 0 14 Dec 2020
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack Rui Shu Tianpei Xia Laurie A. Williams Tim Menzies AAML 24 15 0 23 Nov 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 17 323 0 07 Oct 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 21 11 0 21 Sep 2020
Adversarially Robust Neural Architectures Minjing Dong Yanxi Li Yunhe Wang Chang Xu AAML OOD 42 48 0 02 Sep 2020
On Adversarial Robustness: A Neural Architecture Search perspective Chaitanya Devaguptapu Devansh Agarwal Gaurav Mittal Pulkit Gopalani V. Balasubramanian OOD AAML 12 33 0 16 Jul 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 25 37 0 13 Jul 2020
Diverse Ensembles Improve Calibration Asa Cooper Stickland Iain Murray UQCV FedML 27 26 0 08 Jul 2020
Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples Kaleel Mahmood Deniz Gurevin Marten van Dijk Phuong Ha Nguyen AAML 12 22 0 18 Jun 2020
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation Hong Joo Lee S. T. Kim Hakmin Lee Nassir Navab Yong Man Ro UQCV 16 7 0 21 May 2020
Certifying Joint Adversarial Robustness for Model Ensembles M. Jonas David E. Evans AAML 21 2 0 21 Apr 2020
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Tianlong Chen Sijia Liu Shiyu Chang Yu Cheng Lisa Amini Zhangyang Wang AAML 18 246 0 28 Mar 2020
Deep Neural Network Perception Models and Robust Autonomous Driving Systems M. Shafiee Ahmadreza Jeddi Amir Nazemi Paul Fieguth A. Wong OOD 25 15 0 04 Mar 2020
Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness Ahmadreza Jeddi M. Shafiee Michelle Karg C. Scharfenberger A. Wong OOD AAML 58 63 0 02 Mar 2020
Attacks Which Do Not Kill Training Make Adversarial Learning Stronger Jingfeng Zhang Xilie Xu Bo Han Gang Niu Li-zhen Cui Masashi Sugiyama Mohan S. Kankanhalli AAML 24 396 0 26 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel A. Madry AAML 104 820 0 19 Feb 2020
Benchmarking Adversarial Robustness Yinpeng Dong Qi-An Fu Xiao Yang Tianyu Pang Hang Su Zihao Xiao Jun Zhu AAML 28 36 0 26 Dec 2019
Enhancing Certifiable Robustness via a Deep Model Ensemble Huan Zhang Minhao Cheng Cho-Jui Hsieh 33 9 0 31 Oct 2019
Adversarial Defense by Restricting the Hidden Space of Deep Neural Networks Aamir Mustafa Salman Khan Munawar Hayat Roland Göcke Jianbing Shen Ling Shao AAML 17 151 0 01 Apr 2019
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 273 3,110 0 04 Nov 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 287 5,837 0 08 Jul 2016
Determinantal point processes for machine learning Alex Kulesza B. Taskar 162 1,123 0 25 Jul 2012