Adversarially Robust Generalization Requires More Data

30 April 2018

Papers citing "Adversarially Robust Generalization Requires More Data"

50 / 146 papers shown

Title
Risk Analysis and Design Against Adversarial Actions M. Campi A. Carè Luis G. Crespo S. Garatti Federico A. Ramponi AAML 118 0 0 02 May 2025
Robust Feature Learning for Multi-Index Models in High Dimensions Alireza Mousavi-Hosseini Adel Javanmard Murat A. Erdogdu OOD AAML 42 1 0 21 Oct 2024
Understanding Model Ensemble in Transferable Adversarial Attack Wei Yao Zeliang Zhang Huayi Tang Yong Liu 33 2 0 09 Oct 2024
Mitigating Memorization In Language Models Mansi Sakarvadia Aswathy Ajith Arham Khan Nathaniel Hudson Caleb Geniesse Kyle Chard Yaoqing Yang Ian Foster Michael W. Mahoney KELM MU 53 0 0 03 Oct 2024
Evaluating Model Robustness Using Adaptive Sparse L0 Regularization Weiyou Liu Zhenyang Li Weitong Chen AAML 22 1 0 28 Aug 2024
Adversarial Robustification via Text-to-Image Diffusion Models Daewon Choi Jongheon Jeong Huiwon Jang Jinwoo Shin DiffM 39 1 0 26 Jul 2024
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu AAML 28 0 0 20 Jun 2024
Adversaries With Incentives: A Strategic Alternative to Adversarial Robustness Maayan Ehrenberg Roy Ganz Nir Rosenfeld AAML 45 0 0 17 Jun 2024
Exploiting the Layered Intrinsic Dimensionality of Deep Models for Practical Adversarial Training Enes Altinisik Safa Messaoud H. Sencar Hassan Sajjad Sanjay Chawla AAML 43 0 0 27 May 2024
The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective Nils Philipp Walter Linara Adilova Jilles Vreeken Michael Kamp AAML 43 2 0 27 May 2024
Uniformly Stable Algorithms for Adversarial Training and Beyond Jiancong Xiao Jiawei Zhang Zhimin Luo Asuman Ozdaglar AAML 40 0 0 03 May 2024
From Robustness to Improved Generalization and Calibration in Pre-trained Language Models Josip Jukić Jan Snajder 31 0 0 31 Mar 2024
$Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$-Perturbation$ Asymptotic Behavior of Adversarial Training Estimator under $\ell_\infty$ -Perturbation Yiling Xie Xiaoming Huo 36 2 0 27 Jan 2024
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training Shruthi Gowda Bahram Zonooz Elahe Arani AAML 28 2 0 26 Jan 2024
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off Yatong Bai Brendon G. Anderson Somayeh Sojoudi AAML 22 2 0 26 Nov 2023
On The Relationship Between Universal Adversarial Attacks And Sparse Representations Dana Weitzner Raja Giryes AAML 24 0 0 14 Nov 2023
Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness Ambar Pal Huaijin Hao René Vidal 26 8 0 28 Sep 2023
Distributed Extra-gradient with Optimal Complexity and Communication Guarantees Ali Ramezani-Kebrya Kimon Antonakopoulos Igor Krawczuk Justin Deschenaux V. Cevher 32 2 0 17 Aug 2023
A Theoretical Perspective on Subnetwork Contributions to Adversarial Robustness Jovon Craig Joshua Andle Theodore S. Nowak S. Y. Sekeh AAML 39 0 0 07 Jul 2023
On the Importance of Backbone to the Adversarial Robustness of Object Detectors Xiao-Li Li Hang Chen Xiaolin Hu AAML 38 4 0 27 May 2023
Fantastic DNN Classifiers and How to Identify them without Data Nathaniel R. Dean D. Sarkar 21 1 0 24 May 2023
Reliable learning in challenging environments Maria-Florina Balcan Steve Hanneke Rattana Pukdee Dravyansh Sharma OOD 27 4 0 06 Apr 2023
Probing the Purview of Neural Networks via Gradient Analysis Jinsol Lee Charles Lehman M. Prabhushankar Ghassan AlRegib 29 7 0 06 Apr 2023
Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness Wei Wei Jiahuan Zhou Yingying Wu AAML 13 0 0 29 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 36 34 0 19 Mar 2023
Certified Robust Neural Networks: Generalization and Corruption Resistance Amine Bennouna Ryan Lucas Bart P. G. Van Parys 32 10 0 03 Mar 2023
Characterizing the Optimal 0-1 Loss for Multi-class Classification with a Test-time Attacker Sihui Dai Wen-Luan Ding A. Bhagoji Daniel Cullina Ben Y. Zhao Haitao Zheng Prateek Mittal AAML 27 2 0 21 Feb 2023
Better Diffusion Models Further Improve Adversarial Training Zekai Wang Tianyu Pang Chao Du Min-Bin Lin Weiwei Liu Shuicheng Yan DiffM 16 207 0 09 Feb 2023
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing Yatong Bai Brendon G. Anderson Aerin Kim Somayeh Sojoudi AAML 28 18 0 29 Jan 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 16 50 0 24 Jan 2023
Foundation models in brief: A historical, socio-technical focus Johannes Schneider VLM 23 9 0 17 Dec 2022
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 27 5 0 15 Dec 2022
Malign Overfitting: Interpolation Can Provably Preclude Invariance Yoav Wald G. Yona Uri Shalit Y. Carmon 15 5 0 28 Nov 2022
Adversarial Detection by Approximation of Ensemble Boundary T. Windeatt AAML 24 0 0 18 Nov 2022
Evolution of Neural Tangent Kernels under Benign and Adversarial Training Noel Loo Ramin Hasani Alexander Amini Daniela Rus AAML 28 12 0 21 Oct 2022
Towards Understanding GD with Hard and Conjugate Pseudo-labels for Test-Time Adaptation Jun-Kun Wang Andre Wibisono 27 7 0 18 Oct 2022
Stability Analysis and Generalization Bounds of Adversarial Training Jiancong Xiao Yanbo Fan Ruoyu Sun Jue Wang Zhimin Luo AAML 24 30 0 03 Oct 2022
Adaptive Smoothness-weighted Adversarial Training for Multiple Perturbations with Its Stability Analysis Jiancong Xiao Zeyu Qin Yanbo Fan Baoyuan Wu Jue Wang Zhimin Luo AAML 31 7 0 02 Oct 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 22 2 0 27 Sep 2022
A Light Recipe to Train Robust Vision Transformers Edoardo Debenedetti Vikash Sehwag Prateek Mittal ViT 26 68 0 15 Sep 2022
Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) Zhenyu Zhu Fanghui Liu Grigorios G. Chrysos V. Cevher 39 19 0 15 Sep 2022
AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance Xiaoliang Liu S. Furao Jian Zhao Changhai Nie AAML 13 1 0 21 Jul 2022
Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations Tianlong Chen Peihao Wang Zhiwen Fan Zhangyang Wang 28 55 0 04 Jul 2022
On the Role of Generalization in Transferability of Adversarial Examples Yilin Wang Farzan Farnia AAML 24 10 0 18 Jun 2022
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning Tianlong Chen Sijia Liu Shiyu Chang Lisa Amini Zhangyang Wang CLL 21 4 0 15 Jun 2022
Adversarial Reprogramming Revisited Matthias Englert R. Lazic AAML 21 8 0 07 Jun 2022
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction Ruochen Jiao Xiangguo Liu Takami Sato Qi Alfred Chen Qi Zhu AAML 28 20 0 27 May 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power Binghui Li Jikai Jin Han Zhong J. Hopcroft Liwei Wang OOD 74 27 0 27 May 2022
Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks Siddhartha Datta AAML 30 4 0 15 May 2022
A Simple Approach to Adversarial Robustness in Few-shot Image Classification Akshayvarun Subramanya Hamed Pirsiavash VLM 21 6 0 11 Apr 2022