Robustness to Adversarial Perturbations in Learning from Incomplete Data

24 May 2019

Papers citing "Robustness to Adversarial Perturbations in Learning from Incomplete Data"

32 / 32 papers shown

Title
RideKE: Leveraging Low-Resource, User-Generated Twitter Content for Sentiment and Emotion Detection in Kenyan Code-Switched Dataset Naome A. Etori Maria L. Gini 76 2 0 10 Feb 2025
Better Representations via Adversarial Training in Pre-Training: A Theoretical Perspective Yue Xing Xiaofeng Lin Qifan Song Yi Tian Xu Belinda Zeng Guang Cheng SSL 23 0 0 26 Jan 2024
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training Shruthi Gowda Bahram Zonooz Elahe Arani AAML 31 2 0 26 Jan 2024
Generalist: Decoupling Natural and Robust Generalization Hongjun Wang Yisen Wang OOD AAML 49 14 0 24 Mar 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 21 208 0 09 Feb 2023
Semi-Supervised and Unsupervised Deep Visual Learning: A Survey Yanbei Chen Massimiliano Mancini Xiatian Zhu Zeynep Akata 36 113 0 24 Aug 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 24 0 0 17 Aug 2022
Global-Local Regularization Via Distributional Robustness Hoang Phan Trung Le Trung-Nghia Phung Tu Bui Nhat Ho Dinh Q. Phung OOD 14 12 0 01 Mar 2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training Tu Bui Trung Le Quan Hung Tran He Zhao Dinh Q. Phung AAML OOD 31 42 0 27 Feb 2022
A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability Idan Attias Steve Hanneke Yishay Mansour 30 15 0 11 Feb 2022
On the Convergence and Robustness of Adversarial Training Yisen Wang Xingjun Ma James Bailey Jinfeng Yi Bowen Zhou Quanquan Gu AAML 194 345 0 15 Dec 2021
Assessing Fairness in the Presence of Missing Data Yiliang Zhang Q. Long FaML 26 35 0 07 Dec 2021
Data Augmentation Can Improve Robustness Sylvestre-Alvise Rebuffi Sven Gowal D. A. Calian Florian Stimberg Olivia Wiles Timothy A. Mann AAML 17 269 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 29 17 0 09 Nov 2021
Improving Robustness using Generated Data Sven Gowal Sylvestre-Alvise Rebuffi Olivia Wiles Florian Stimberg D. A. Calian Timothy A. Mann 27 293 0 18 Oct 2021
Distributionally Robust Learning Ruidi Chen I. Paschalidis OOD 25 65 0 20 Aug 2021
Improving Global Adversarial Robustness Generalization With Adversarially Trained GAN Desheng Wang Wei-dong Jin Yunpu Wu Aamir Khan GAN 25 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 27 268 0 02 Mar 2021
Guided Interpolation for Adversarial Training Chen Chen Jingfeng Zhang Xilie Xu Tianlei Hu Gang Niu Gang Chen Masashi Sugiyama AAML 19 10 0 15 Feb 2021
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 29 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 17 323 0 07 Oct 2020
Understanding Catastrophic Overfitting in Single-step Adversarial Training Hoki Kim Woojin Lee Jaewook Lee AAML 9 107 0 05 Oct 2020
Label Smoothing and Adversarial Robustness Chaohao Fu Hongbin Chen Na Ruan Weijia Jia AAML 8 12 0 17 Sep 2020
Measuring Robustness to Natural Distribution Shifts in Image Classification Rohan Taori Achal Dave Vaishaal Shankar Nicholas Carlini Benjamin Recht Ludwig Schmidt OOD 22 530 0 01 Jul 2020
Understanding Self-Training for Gradual Domain Adaptation Ananya Kumar Tengyu Ma Percy Liang CLL TTA 28 226 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 22 396 0 26 Feb 2020
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization Yifei Min Lin Chen Amin Karbasi AAML 28 69 0 25 Feb 2020
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen Yifei Min Mingrui Zhang Amin Karbasi OOD 27 64 0 11 Feb 2020
Where is the Bottleneck of Adversarial Learning with Unlabeled Data? Jingfeng Zhang Bo Han Gang Niu Tongliang Liu Masashi Sugiyama 21 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 42 2,358 0 11 Nov 2019
Adversarial Training Can Hurt Generalization Aditi Raghunathan Sang Michael Xie Fanny Yang John C. Duchi Percy Liang 13 239 0 14 Jun 2019
On the Convergence of the EM Algorithm: A Data-Adaptive Analysis Chong Wu Can Yang Hongyu Zhao Ji Zhu 33 19 0 02 Nov 2016