Unlabeled Data Improves Adversarial Robustness

31 May 2019

Papers citing "Unlabeled Data Improves Adversarial Robustness"

50 / 225 papers shown

Title
Why adversarial training can hurt robust accuracy Jacob Clarysse Julia Hörrmann Fanny Yang AAML 15 18 0 03 Mar 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses Francesco Croce Sven Gowal T. Brunner Evan Shelhamer Matthias Hein A. Cemgil TTA AAML 181 68 0 28 Feb 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min Lin Xiao Yang Junyi Zhu Shuicheng Yan 37 120 0 21 Feb 2022
Holistic Adversarial Robustness of Deep Learning Models Pin-Yu Chen Sijia Liu AAML 51 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 35 15 0 30 Jan 2022
Scale-Invariant Adversarial Attack for Evaluating and Enhancing Adversarial Defenses Mengting Xu Tao Zhang Zhongnian Li Daoqiang Zhang AAML 38 1 0 29 Jan 2022
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis Shuai Zhang Ming Wang Sijia Liu Pin-Yu Chen Jinjun Xiong SSL MLT 41 23 0 21 Jan 2022
Benign Overfitting in Adversarially Robust Linear Classification Jinghui Chen Yuan Cao Quanquan Gu AAML SILM 34 10 0 31 Dec 2021
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks Weiran Lin Keane Lucas Lujo Bauer Michael K. Reiter Mahmood Sharif AAML 31 5 0 28 Dec 2021
Improving Robustness with Image Filtering M. Terzi Mattia Carletti Gian Antonio Susto AAML 29 0 0 21 Dec 2021
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
Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness Konstantinos P. Panousis S. Chatzis Sergios Theodoridis BDL AAML 66 11 0 05 Dec 2021
Pyramid Adversarial Training Improves ViT Performance Charles Herrmann Kyle Sargent Lu Jiang Ramin Zabih Huiwen Chang Ce Liu Dilip Krishnan Deqing Sun ViT 32 56 0 30 Nov 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
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
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? Lijie Fan Sijia Liu Pin-Yu Chen Gaoyuan Zhang Chuang Gan AAML VLM 22 120 0 01 Nov 2021
Towards Evaluating the Robustness of Neural Networks Learned by Transduction Jiefeng Chen Xi Wu Yang Guo Yingyu Liang S. Jha ELM AAML 23 15 0 27 Oct 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
Parameterizing Activation Functions for Adversarial Robustness Sihui Dai Saeed Mahloujifar Prateek Mittal AAML 44 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 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 66 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 38 15 0 21 Sep 2021
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 40 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 29 21 0 20 Aug 2021
Distributionally Robust Learning Ruidi Chen I. Paschalidis OOD 27 65 0 20 Aug 2021
MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation Pan Xie Zexian Li Xiaohui Hu 34 11 0 19 Aug 2021
Federated Asymptotics: a model to compare federated learning algorithms Gary Cheng Karan N. Chadha John C. Duchi FedML 19 16 0 16 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 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
On the Certified Robustness for Ensemble Models and Beyond Zhuolin Yang Linyi Li Xiaojun Xu B. Kailkhura Tao Xie Bo-wen Li AAML 29 48 0 22 Jul 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 197 0 12 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 29 4 0 06 Jul 2021
The Values Encoded in Machine Learning Research Abeba Birhane Pratyusha Kalluri Dallas Card William Agnew Ravit Dotan Michelle Bao 41 275 0 29 Jun 2021
Adversarial Training Helps Transfer Learning via Better Representations Zhun Deng Linjun Zhang Kailas Vodrahalli Kenji Kawaguchi James Zou GAN 36 53 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 31 23 0 12 Jun 2021
Generate, Annotate, and Learn: NLP with Synthetic Text Xuanli He Islam Nassar J. Kiros Gholamreza Haffari Mohammad Norouzi 39 51 0 11 Jun 2021
Adversarial purification with Score-based generative models Jongmin Yoon Sung Ju Hwang Juho Lee DiffM 30 153 0 11 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
Analysis and Applications of Class-wise Robustness in Adversarial Training Qi Tian Kun Kuang Ke Jiang Fei Wu Yisen Wang AAML 20 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 60 0 21 May 2021
Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attacks Dequan Wang An Ju Evan Shelhamer David Wagner Trevor Darrell AAML 26 27 0 18 May 2021