Improving Robustness using Generated Data

18 October 2021

Sven Gowal

Sylvestre-Alvise Rebuffi

Papers citing "Improving Robustness using Generated Data"

50 / 56 papers shown

Title
Diffusion-based Adversarial Purification from the Perspective of the Frequency Domain Gaozheng Pei Ke Ma Yingfei Sun Qianqian Xu Q. Huang DiffM 40 0 0 02 May 2025
Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models Patrick Müller Alexander Braun M. Keuper 50 0 0 25 Apr 2025
Carefully Blending Adversarial Training, Purification, and Aggregation Improves Adversarial Robustness Emanuele Ballarin A. Ansuini Luca Bortolussi AAML 62 0 0 20 Feb 2025
Artificial Kuramoto Oscillatory Neurons Takeru Miyato Sindy Lowe Andreas Geiger Max Welling AI4CE 67 6 0 17 Feb 2025
Does Training on Synthetic Data Make Models Less Robust? Lingze Zhang Ellie Pavlick SyDa 84 0 0 11 Feb 2025
MOS-Attack: A Scalable Multi-objective Adversarial Attack Framework Ping Guo Cheng Gong Xi Victoria Lin Fei Liu Zhichao Lu Qingfu Zhang Zhenkun Wang AAML 36 0 0 13 Jan 2025
Towards Million-Scale Adversarial Robustness Evaluation With Stronger Individual Attacks Yong Xie Weijie Zheng Hanxun Huang Guangnan Ye Xingjun Ma AAML 72 1 0 20 Nov 2024
FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training Tejaswini Medi Steffen Jung M. Keuper AAML 29 3 0 30 Oct 2024
Privacy-preserving datasets by capturing feature distributions with Conditional VAEs Francesco Di Salvo David Tafler Sebastian Doerrich Christian Ledig CML 28 0 0 01 Aug 2024
ADBM: Adversarial diffusion bridge model for reliable adversarial purification Xiao-Li Li Wenxuan Sun Huanran Chen Qiongxiu Li Yining Liu Yingzhe He Jie Shi Xiaolin Hu AAML 48 7 0 01 Aug 2024
Adversarial Robustification via Text-to-Image Diffusion Models Daewon Choi Jongheon Jeong Huiwon Jang Jinwoo Shin DiffM 30 1 0 26 Jul 2024
PartImageNet++ Dataset: Scaling up Part-based Models for Robust Recognition Xiao-Li Li Yining Liu Na Dong Sitian Qin Xiaolin Hu 34 3 0 15 Jul 2024
Specification Overfitting in Artificial Intelligence Benjamin Roth Pedro Henrique Luz de Araujo Yuxi Xia Saskia Kaltenbrunner Christoph Korab 56 0 0 13 Mar 2024
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 8 0 0 26 Jan 2024
MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness Xiaoyun Xu Shujian Yu Jingzheng Wu S. Picek AAML 33 0 0 08 Dec 2023
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images Zhuoran Yu Chenchen Zhu Sean Culatana Raghuraman Krishnamoorthi Fanyi Xiao Yong Jae Lee 109 13 0 04 Dec 2023
Mixing Classifiers to Alleviate the Accuracy-Robustness Trade-Off Yatong Bai Brendon G. Anderson Somayeh Sojoudi AAML 19 2 0 26 Nov 2023
On The Relationship Between Universal Adversarial Attacks And Sparse Representations Dana Weitzner Raja Giryes AAML 19 0 0 14 Nov 2023
Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods A. Ma Yangchen Pan Amir-massoud Farahmand AAML 25 5 0 13 Aug 2023
Doubly Robust Instance-Reweighted Adversarial Training Daouda Sow Sen-Fon Lin Zhangyang Wang Yitao Liang AAML OOD 28 2 0 01 Aug 2023
DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce Ipsita Mohanty 14 2 0 21 Jul 2023
Mitigating Adversarial Vulnerability through Causal Parameter Estimation by Adversarial Double Machine Learning Byung-Kwan Lee Junho Kim Yonghyun Ro AAML 10 9 0 14 Jul 2023
Enhancing Adversarial Robustness via Score-Based Optimization Boya Zhang Weijian Luo Zhihua Zhang DiffM 19 12 0 10 Jul 2023
Group-based Robustness: A General Framework for Customized Robustness in the Real World Weiran Lin Keane Lucas Neo Eyal Lujo Bauer Michael K. Reiter Mahmood Sharif OOD AAML 22 1 0 29 Jun 2023
On the Importance of Backbone to the Adversarial Robustness of Object Detectors Xiao-Li Li Hang Chen Xiaolin Hu AAML 34 4 0 27 May 2023
How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses Joana Cabral Costa Tiago Roxo Hugo Manuel Proença Pedro R. M. Inácio AAML 30 48 0 18 May 2023
Raising the Bar for Certified Adversarial Robustness with Diffusion Models Thomas Altstidl David Dobre Björn Eskofier Gauthier Gidel Leo Schwinn DiffM 20 7 0 17 May 2023
Utility Theory of Synthetic Data Generation Shi Xu W. Sun Guang Cheng 15 5 0 17 May 2023
An Extended Study of Human-like Behavior under Adversarial Training Paul Gavrikov J. Keuper M. Keuper AAML 21 9 0 22 Mar 2023
Randomized Adversarial Training via Taylor Expansion Gao Jin Xinping Yi Dengyu Wu Ronghui Mu Xiaowei Huang AAML 25 34 0 19 Mar 2023
MultiRobustBench: Benchmarking Robustness Against Multiple Attacks Sihui Dai Saeed Mahloujifar Chong Xiang Vikash Sehwag Pin-Yu Chen Prateek Mittal AAML OOD 14 7 0 21 Feb 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 14 207 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 19 18 0 29 Jan 2023
Data Augmentation Alone Can Improve Adversarial Training Lin Li Michael W. Spratling 16 49 0 24 Jan 2023
Alternating Objectives Generates Stronger PGD-Based Adversarial Attacks Nikolaos Antoniou Efthymios Georgiou Alexandros Potamianos AAML 22 5 0 15 Dec 2022
Learning Sample Reweighting for Accuracy and Adversarial Robustness Chester Holtz Tsui-Wei Weng Gal Mishne OOD 24 4 0 20 Oct 2022
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper M. Keuper AAML 14 24 0 12 Oct 2022
Boosting Adversarial Robustness From The Perspective of Effective Margin Regularization Ziquan Liu Antoni B. Chan AAML 25 5 0 11 Oct 2022
Inducing Data Amplification Using Auxiliary Datasets in Adversarial Training Saehyung Lee Hyungyu Lee AAML 22 2 0 27 Sep 2022
Bag of Tricks for FGSM Adversarial Training Zichao Li Li Liu Zeyu Wang Yuyin Zhou Cihang Xie AAML 14 6 0 06 Sep 2022
Constraining Representations Yields Models That Know What They Don't Know João Monteiro Pau Rodríguez López Pierre-Andre Noel I. Laradji David Vazquez AAML 28 0 0 30 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 16 0 0 17 Aug 2022
Increasing Confidence in Adversarial Robustness Evaluations Roland S. Zimmermann Wieland Brendel Florian Tramèr Nicholas Carlini AAML 36 16 0 28 Jun 2022
Semi-supervised Semantics-guided Adversarial Training for Trajectory Prediction Ruochen Jiao Xiangguo Liu Takami Sato Qi Alfred Chen Qi Zhu AAML 23 20 0 27 May 2022
Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks Sizhe Chen Zhehao Huang Qinghua Tao Yingwen Wu Cihang Xie X. Huang AAML 106 28 0 24 May 2022
Diffusion Models for Adversarial Purification Weili Nie Brandon Guo Yujia Huang Chaowei Xiao Arash Vahdat Anima Anandkumar WIGM 195 415 0 16 May 2022
Adversarial Robustness through the Lens of Convolutional Filters Paul Gavrikov J. Keuper 25 15 0 05 Apr 2022
Generating High Fidelity Data from Low-density Regions using Diffusion Models Vikash Sehwag C. Hazirbas Albert Gordo Firat Ozgenel Cristian Canton Ferrer DiffM 25 66 0 31 Mar 2022
Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks A. Harrington Arturo Deza OOD AAML 21 20 0 02 Feb 2022