Are Labels Required for Improving Adversarial Robustness?

31 May 2019

J. Uesato

Jean-Baptiste Alayrac

Papers citing "Are Labels Required for Improving Adversarial Robustness?"

50 / 108 papers shown

Title
TAROT: Towards Essentially Domain-Invariant Robustness with Theoretical Justification Dongyoon Yang Jihu Lee Yongdai Kim 34 0 0 10 May 2025
Revisiting the Relationship between Adversarial and Clean Training: Why Clean Training Can Make Adversarial Training Better MingWei Zhou Xiaobing Pei AAML 241 0 0 30 Mar 2025
Weakly Supervised Contrastive Adversarial Training for Learning Robust Features from Semi-supervised Data Lilin Zhang Chengpei Wu Ning Yang 39 0 0 14 Mar 2025
Generating Less Certain Adversarial Examples Improves Robust Generalization Minxing Zhang Michael Backes Xiao Zhang AAML 42 1 0 06 Oct 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 54 51 0 18 May 2023
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning Jun Shu Xiang Yuan Deyu Meng Zongben Xu 35 4 0 13 May 2023
Federated Learning without Full Labels: A Survey Yilun Jin Yang Liu Kai Chen Qian Yang FedML 19 26 0 25 Mar 2023
Generalist: Decoupling Natural and Robust Generalization Hongjun Wang Yisen Wang OOD AAML 49 14 0 24 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 44 34 0 19 Mar 2023
It Is All About Data: A Survey on the Effects of Data on Adversarial Robustness Peiyu Xiong Michael W. Tegegn Jaskeerat Singh Sarin Shubhraneel Pal Julia Rubin SILM AAML 37 8 0 17 Mar 2023
Certified Robust Neural Networks: Generalization and Corruption Resistance Amine Bennouna Ryan Lucas Bart P. G. Van Parys 48 10 0 03 Mar 2023
Backdoor Learning for NLP: Recent Advances, Challenges, and Future Research Directions Marwan Omar SILM AAML 37 20 0 14 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min Lin Weiwei Liu Shuicheng Yan DiffM 26 210 0 09 Feb 2023
Efficient Adversarial Contrastive Learning via Robustness-Aware Coreset Selection Xilie Xu Jingfeng Zhang Feng Liu Masashi Sugiyama Mohan S. Kankanhalli AAML 26 16 0 08 Feb 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 21 50 0 24 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 29 5 0 15 Dec 2022
Generative Robust Classification Xuwang Yin TPM 33 0 0 14 Dec 2022
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models Chengzhi Mao Scott Geng Junfeng Yang Xin Eric Wang Carl Vondrick VLM 44 60 0 14 Dec 2022
Deep Fake Detection, Deterrence and Response: Challenges and Opportunities Amin Azmoodeh Ali Dehghantanha 45 2 0 26 Nov 2022
Reliable Robustness Evaluation via Automatically Constructed Attack Ensembles Shengcai Liu Fu Peng Jiaheng Zhang AAML 39 11 0 23 Nov 2022
Improving Adversarial Robustness with Self-Paced Hard-Class Pair Reweighting Peng-Fei Hou Jie Han Xingyu Li AAML OOD 23 11 0 26 Oct 2022
On the Adversarial Robustness of Mixture of Experts J. Puigcerver Rodolphe Jenatton C. Riquelme Pranjal Awasthi Srinadh Bhojanapalli OOD AAML MoE 48 18 0 19 Oct 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 29 2 0 27 Sep 2022
Two Heads are Better than One: Robust Learning Meets Multi-branch Models Dong Huang Qi Bu Yuhao Qing Haowen Pi Sen Wang Heming Cui OOD AAML 39 0 0 17 Aug 2022
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness Chaoning Zhang Kang Zhang Chenshuang Zhang Axi Niu Jiu Feng Chang D. Yoo In So Kweon SSL 40 24 0 22 Jul 2022
Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift Ananya Kumar Tengyu Ma Percy Liang Aditi Raghunathan UQCV OODD OOD 49 38 0 18 Jul 2022
Adversarial Contrastive Learning via Asymmetric InfoNCE Qiying Yu Jieming Lou Xianyuan Zhan Qizhang Li W. Zuo Yang Liu Jingjing Liu AAML 41 23 0 18 Jul 2022
RUSH: Robust Contrastive Learning via Randomized Smoothing Yijiang Pang Boyang Liu Jiayu Zhou OOD AAML 24 1 0 11 Jul 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen Sijia Liu Shiyu Chang Lisa Amini Zhangyang Wang CLL 26 4 0 15 Jun 2022
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale Gaoyuan Zhang Songtao Lu Yihua Zhang Xiangyi Chen Pin-Yu Chen Quanfu Fan Lee Martie L. Horesh Min-Fong Hong Sijia Liu OOD 35 12 0 13 Jun 2022
Robust Weight Perturbation for Adversarial Training Chaojian Yu Bo Han Biwei Huang Li Shen Shiming Ge Bo Du Tongliang Liu AAML 27 33 0 30 May 2022
How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspective Yimeng Zhang Yuguang Yao Jinghan Jia Jinfeng Yi Min-Fong Hong Shiyu Chang Sijia Liu AAML 28 33 0 27 Mar 2022
Robustness through Cognitive Dissociation Mitigation in Contrastive Adversarial Training Adir Rahamim I. Naeh AAML 35 1 0 16 Mar 2022
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 15 18 0 03 Mar 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min Lin Xiao Yang Junyi Zhu Shuicheng Yan 40 120 0 21 Feb 2022
Holistic Adversarial Robustness of Deep Learning Models Pin-Yu Chen Sijia Liu AAML 54 16 0 15 Feb 2022
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability Idan Attias Steve Hanneke Yishay Mansour 35 15 0 11 Feb 2022
Backdoor Defense via Decoupling the Training Process Kunzhe Huang Yiming Li Baoyuan Wu Zhan Qin Kui Ren AAML FedML 29 187 0 05 Feb 2022
Robust Binary Models by Pruning Randomly-initialized Networks Chen Liu Ziqi Zhao Sabine Süsstrunk Mathieu Salzmann TPM AAML MQ 32 4 0 03 Feb 2022
Improving Robustness by Enhancing Weak Subnets Yong Guo David Stutz Bernt Schiele AAML 37 15 0 30 Jan 2022
On the Impact of Hard Adversarial Instances on Overfitting in Adversarial Training Chen Liu Zhichao Huang Mathieu Salzmann Tong Zhang Sabine Süsstrunk AAML 28 13 0 14 Dec 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 34 271 0 09 Nov 2021
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps Muhammad Awais Fengwei Zhou Chuanlong Xie Jiawei Li Sung-Ho Bae Zhenguo Li AAML 43 17 0 09 Nov 2021
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 27 26 0 03 Nov 2021
Transductive Robust Learning Guarantees Omar Montasser Steve Hanneke Nathan Srebro 26 13 0 20 Oct 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 36 294 0 18 Oct 2021
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning Guanlin Liu Lifeng Lai AAML 32 34 0 09 Oct 2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks Hanxun Huang Yisen Wang S. Erfani Quanquan Gu James Bailey Xingjun Ma AAML TPM 48 100 0 07 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Chengyu Dong Liyuan Liu Jingbo Shang NoLa AAML 69 18 0 07 Oct 2021
Distributionally Robust Learning Ruidi Chen I. Paschalidis OOD 32 65 0 20 Aug 2021