Unlabeled Data Improves Adversarial Robustness

31 May 2019

Papers citing "Unlabeled Data Improves Adversarial Robustness"

50 / 193 papers shown

Title
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 37 17 0 09 Nov 2021
LTD: Low Temperature Distillation for Robust Adversarial Training Erh-Chung Chen Che-Rung Lee AAML 24 26 0 03 Nov 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks Maksym Yatsura J. H. Metzen Matthias Hein OOD 26 14 0 02 Nov 2021
Transductive Robust Learning Guarantees Omar Montasser Steve Hanneke Nathan Srebro 16 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 30 293 0 18 Oct 2021
Parameterizing Activation Functions for Adversarial Robustness Sihui Dai Saeed Mahloujifar Prateek Mittal AAML 42 32 0 11 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 46 100 0 07 Oct 2021
Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting Chengyu Dong Liyuan Liu Jingbo Shang NoLa AAML 56 18 0 07 Oct 2021
Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning Haiyun He Hanshu Yan Vincent Y. F. Tan 34 11 0 03 Oct 2021
Modeling Adversarial Noise for Adversarial Training Dawei Zhou Nannan Wang Bo Han Tongliang Liu AAML 32 15 0 21 Sep 2021
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 35 4 0 16 Sep 2021
Towards Understanding the Generative Capability of Adversarially Robust Classifiers Yao Zhu Jiacheng Ma Jiacheng Sun Zewei Chen Rongxin Jiang Zhenguo Li AAML 18 21 0 20 Aug 2021
Distributionally Robust Learning Ruidi Chen I. Paschalidis OOD 25 65 0 20 Aug 2021
MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation Pan Xie Zexian Li Xiaohui Hu 31 11 0 19 Aug 2021
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 19 15 0 13 Aug 2021
Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Them Florian Tramèr AAML 30 64 0 24 Jul 2021
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Bo-wen Li AAML 26 48 0 22 Jul 2021
ROPUST: Improving Robustness through Fine-tuning with Photonic Processors and Synthetic Gradients Alessandro Cappelli Julien Launay Laurent Meunier Ruben Ohana Iacopo Poli AAML 16 4 0 06 Jul 2021
The Values Encoded in Machine Learning Research Abeba Birhane Pratyusha Kalluri Dallas Card William Agnew Ravit Dotan Michelle Bao 25 274 0 29 Jun 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 36 52 0 18 Jun 2021
Consistency Regularization for Cross-Lingual Fine-Tuning Bo Zheng Li Dong Shaohan Huang Wenhui Wang Zewen Chi Saksham Singhal Wanxiang Che Ting Liu Xia Song Furu Wei 19 58 0 15 Jun 2021
Adversarial Robustness via Fisher-Rao Regularization Marine Picot Francisco Messina Malik Boudiaf Fabrice Labeau Ismail Ben Ayed Pablo Piantanida AAML 23 23 0 12 Jun 2021
Generate, Annotate, and Learn: NLP with Synthetic Text Xuanli He Islam Nassar J. Kiros Gholamreza Haffari Mohammad Norouzi 33 51 0 11 Jun 2021
Adversarial purification with Score-based generative models Jongmin Yoon Sung Ju Hwang Juho Lee DiffM 14 151 0 11 Jun 2021
Analysis and Applications of Class-wise Robustness in Adversarial Training Qi Tian Kun Kuang Ke Jiang Fei Wu Yisen Wang AAML 18 46 0 29 May 2021
Exploring Misclassifications of Robust Neural Networks to Enhance Adversarial Attacks Leo Schwinn René Raab A. Nguyen Dario Zanca Bjoern M. Eskofier AAML 14 58 0 21 May 2021
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks Dequan Wang An Ju Evan Shelhamer David A. Wagner Trevor Darrell AAML 26 26 0 18 May 2021
Vision Transformers are Robust Learners Sayak Paul Pin-Yu Chen ViT 22 304 0 17 May 2021
LAFEAT: Piercing Through Adversarial Defenses with Latent Features Yunrui Yu Xitong Gao Chengzhong Xu AAML FedML 25 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 22 18 0 16 Apr 2021
Relating Adversarially Robust Generalization to Flat Minima David Stutz Matthias Hein Bernt Schiele OOD 29 65 0 09 Apr 2021
Adversarial Robustness under Long-Tailed Distribution Tong Wu Ziwei Liu Qingqiu Huang Yu Wang Dahua Lin 18 76 0 06 Apr 2021
Combating Adversaries with Anti-Adversaries Motasem Alfarra Juan C. Pérez Ali K. Thabet Adel Bibi Philip H. S. Torr Bernard Ghanem AAML 26 26 0 26 Mar 2021
StyleLess layer: Improving robustness for real-world driving Julien Rebut Andrei Bursuc P. Pérez 22 5 0 25 Mar 2021
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN Desheng Wang Wei-dong Jin Yunpu Wu Aamir Khan GAN 28 8 0 08 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 33 268 0 02 Mar 2021
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training Dorjan Hitaj Giulio Pagnotta I. Masi L. Mancini OOD AAML 18 22 0 02 Mar 2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints Maura Pintor Fabio Roli Wieland Brendel Battista Biggio AAML 43 70 0 25 Feb 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 15 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 27 10 0 15 Feb 2021
Mixed Nash Equilibria in the Adversarial Examples Game Laurent Meunier M. Scetbon Rafael Pinot Jamal Atif Y. Chevaleyre AAML 15 29 0 13 Feb 2021
When and How Mixup Improves Calibration Linjun Zhang Zhun Deng Kenji Kawaguchi James Zou UQCV 28 67 0 11 Feb 2021
Proof Artifact Co-training for Theorem Proving with Language Models Jesse Michael Han Jason M. Rute Yuhuai Wu Edward W. Ayers Stanislas Polu AIMat 23 120 0 11 Feb 2021
Understanding the Interaction of Adversarial Training with Noisy Labels Jianing Zhu Jingfeng Zhang Bo Han Tongliang Liu Gang Niu Hongxia Yang Mohan S. Kankanhalli Masashi Sugiyama AAML 19 27 0 06 Feb 2021
Understanding and Increasing Efficiency of Frank-Wolfe Adversarial Training Theodoros Tsiligkaridis Jay Roberts AAML 17 11 0 22 Dec 2020
Self-Progressing Robust Training Minhao Cheng Pin-Yu Chen Sijia Liu Shiyu Chang Cho-Jui Hsieh Payel Das AAML VLM 21 9 0 22 Dec 2020
Composite Adversarial Attacks Xiaofeng Mao YueFeng Chen Shuhui Wang Hang Su Yuan He Hui Xue AAML 30 47 0 10 Dec 2020
Data-Dependent Randomized Smoothing Motasem Alfarra Adel Bibi Philip H. S. Torr Bernard Ghanem UQCV 23 34 0 08 Dec 2020
Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Gaurang Sriramanan Sravanti Addepalli Arya Baburaj R. Venkatesh Babu AAML 8 92 0 30 Nov 2020