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
AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning Hong Wang Yuefan Deng Shinjae Yoo Haibin Ling Yuewei Lin AAML 32 15 0 13 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 65 0 24 Jul 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 36 54 0 18 Jun 2021
Adversarial Robustness via Fisher-Rao Regularization Marine Picot Francisco Messina Malik Boudiaf Fabrice Labeau Ismail Ben Ayed Pablo Piantanida AAML 31 23 0 12 Jun 2021
NoiLIn: Improving Adversarial Training and Correcting Stereotype of Noisy Labels Jingfeng Zhang Xilie Xu Bo Han Tongliang Liu Gang Niu Li-zhen Cui Masashi Sugiyama NoLa AAML 23 9 0 31 May 2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features Yunrui Yu Xitong Gao Chengzhong Xu AAML FedML 33 44 0 19 Apr 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele AAML MQ 24 18 0 16 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 41 65 0 09 Apr 2021
Adversarial Attacks are Reversible with Natural Supervision Chengzhi Mao Mia Chiquer Hao Wang Junfeng Yang Carl Vondrick BDL AAML 21 55 0 26 Mar 2021
Deepfake Forensics via An Adversarial Game Zhi Wang Yiwen Guo W. Zuo AAML 21 35 0 25 Mar 2021
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN Desheng Wang Wei-dong Jin Yunpu Wu Aamir Khan GAN 36 8 0 08 Mar 2021
Towards Evaluating the Robustness of Deep Diagnostic Models by Adversarial Attack Mengting Xu Tao Zhang Zhongnian Li Mingxia Liu Daoqiang Zhang AAML OOD MedIm 33 41 0 05 Mar 2021
Fixing Data Augmentation to Improve Adversarial Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 36 271 0 02 Mar 2021
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 26 47 0 20 Feb 2021
Guided Interpolation for Adversarial Training Chen Chen Jingfeng Zhang Xilie Xu Tianlei Hu Gang Niu Gang Chen Masashi Sugiyama AAML 37 10 0 15 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan Kankanhalli Masashi Sugiyama AAML 27 27 0 06 Feb 2021
Recent Advances in Adversarial Training for Adversarial Robustness Tao Bai Jinqi Luo Jun Zhao Bihan Wen Qian Wang AAML 86 476 0 02 Feb 2021
Self-Progressing Robust Training Minhao Cheng Pin-Yu Chen Sijia Liu Shiyu Chang Cho-Jui Hsieh Payel Das AAML VLM 29 9 0 22 Dec 2020
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang Tianlong Chen Ting-Li Chen Zhangyang Wang 30 228 0 26 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 234 681 0 19 Oct 2020
Optimism in the Face of Adversity: Understanding and Improving Deep Learning through Adversarial Robustness Guillermo Ortiz-Jiménez Apostolos Modas Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 39 48 0 19 Oct 2020
Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples Sven Gowal Chongli Qin J. Uesato Timothy A. Mann Pushmeet Kohli AAML 22 325 0 07 Oct 2020
Understanding Catastrophic Overfitting in Single-step Adversarial Training Hoki Kim Woojin Lee Jaewook Lee AAML 16 108 0 05 Oct 2020
Adversarial Robustness of Stabilized NeuralODEs Might be from Obfuscated Gradients Yifei Huang Yaodong Yu Hongyang R. Zhang Yi Ma Yuan Yao AAML 37 26 0 28 Sep 2020
Adversarial Training with Stochastic Weight Average Joong-won Hwang Youngwan Lee Sungchan Oh Yuseok Bae OOD AAML 31 11 0 21 Sep 2020
Label Smoothing and Adversarial Robustness Chaohao Fu Hongbin Chen Na Ruan Weijia Jia AAML 16 12 0 17 Sep 2020
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning Jirong Yi R. Mudumbai Weiyu Xu AAML 32 2 0 28 Jul 2020
Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song Minseok Kim Dongmin Park Yooju Shin Jae-Gil Lee NoLa 24 964 0 16 Jul 2020
Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples S. Goldwasser Adam Tauman Kalai Y. Kalai Omar Montasser AAML 22 38 0 10 Jul 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 50 537 0 01 Jul 2020
Sharp Statistical Guarantees for Adversarially Robust Gaussian Classification Chen Dan Yuting Wei Pradeep Ravikumar 26 45 0 29 Jun 2020
Statistical and Algorithmic Insights for Semi-supervised Learning with Self-training Samet Oymak Talha Cihad Gulcu 27 20 0 19 Jun 2020
Self-training Avoids Using Spurious Features Under Domain Shift Yining Chen Colin Wei Ananya Kumar Tengyu Ma OOD 29 85 0 17 Jun 2020
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them Chen Liu Mathieu Salzmann Tao R. Lin Ryota Tomioka Sabine Süsstrunk AAML 24 81 0 15 Jun 2020
Adversarial Self-Supervised Contrastive Learning Minseon Kim Jihoon Tack Sung Ju Hwang SSL 28 247 0 13 Jun 2020
Large-Scale Adversarial Training for Vision-and-Language Representation Learning Zhe Gan Yen-Chun Chen Linjie Li Chen Zhu Yu Cheng Jingjing Liu ObjD VLM 35 489 0 11 Jun 2020
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers S. Melacci Gabriele Ciravegna Angelo Sotgiu Ambra Demontis Battista Biggio Marco Gori Fabio Roli 22 14 0 06 Jun 2020
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 19 92 0 05 May 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
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 99 0 20 Mar 2020
Toward Adversarial Robustness via Semi-supervised Robust Training Yiming Li Baoyuan Wu Yan Feng Yanbo Fan Yong Jiang Zhifeng Li Shutao Xia AAML 87 13 0 16 Mar 2020
Diversity can be Transferred: Output Diversification for White- and Black-box Attacks Y. Tashiro Yang Song Stefano Ermon AAML 14 13 0 15 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 72 63 0 02 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 47 788 0 26 Feb 2020
Understanding Self-Training for Gradual Domain Adaptation Ananya Kumar Tengyu Ma Percy Liang CLL TTA 28 228 0 26 Feb 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 33 398 0 26 Feb 2020
Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang AAML 51 224 0 25 Feb 2020
MACER: Attack-free and Scalable Robust Training via Maximizing Certified Radius Runtian Zhai Chen Dan Di He Huan Zhang Boqing Gong Pradeep Ravikumar Cho-Jui Hsieh Liwei Wang OOD AAML 27 177 0 08 Jan 2020
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? Jingfeng Zhang Bo Han Gang Niu Tongliang Liu Masashi Sugiyama 30 6 0 20 Nov 2019
Self-training with Noisy Student improves ImageNet classification Qizhe Xie Minh-Thang Luong Eduard H. Hovy Quoc V. Le NoLa 88 2,365 0 11 Nov 2019