Towards Adversarially Robust Dataset Distillation by Curvature Regularization

15 March 2024

Papers citing "Towards Adversarially Robust Dataset Distillation by Curvature Regularization"

50 / 51 papers shown

Title
Robust Dataset Distillation by Matching Adversarial Trajectories Wei Lai Tianyu Ding ren dongdong Lei Wang Jing Huo Yang Gao Wenbin Li AAML DD 79 0 0 15 Mar 2025
Dataset Distillation via Committee Voting Jiacheng Cui Zhaoyi Li Xiaochen Ma Xinyue Bi Yaxin Luo Zhiqiang Shen DD FedML 114 2 0 13 Jan 2025
Breaking Class Barriers: Efficient Dataset Distillation via Inter-Class Feature Compensator Xin Zhang Jiawei Du Ping Liu Joey Tianyi Zhou DD 104 2 0 13 Aug 2024
BACON: Bayesian Optimal Condensation Framework for Dataset Distillation Zheng Zhou Hong Zhao Guangliang Cheng Xiangtai Li Shuchang Lyu Wenquan Feng Qi Zhao DD 77 0 0 03 Jun 2024
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation Yifan Wu Jiawei Du Ping Liu Yuewei Lin Wenqing Cheng Wei Xu DD AAML 68 5 0 20 Mar 2024
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers Haoyang Liu Aditya Singh Yijiang Li Haohan Wang AAML ViT 92 1 0 15 Mar 2024
Group Distributionally Robust Dataset Distillation with Risk Minimization Saeed Vahidian Mingyu Wang Jianyang Gu Vyacheslav Kungurtsev Wei Jiang Yiran Chen OOD DD 83 6 0 07 Feb 2024
M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy Hansong Zhang Shikun Li Pengju Wang Dan Zeng Shiming Ge DD 74 23 0 26 Dec 2023
Dataset Distillation via the Wasserstein Metric Haoyang Liu Yijiang Li Tiancheng Xing Vibhu Dalal Luwei Li Jingrui He Haohan Wang DD 96 14 0 30 Nov 2023
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives Haoyang Liu Maheep Chaudhary Haohan Wang 34 26 0 31 Jul 2023
Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses G. Buzaglo Niv Haim Gilad Yehudai Gal Vardi Yakir Oz Yaniv Nikankin Michal Irani 65 12 0 04 Jul 2023
Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective Zeyuan Yin Eric P. Xing Zhiqiang Shen DD 48 77 0 22 Jun 2023
A Comprehensive Study on Dataset Distillation: Performance, Privacy, Robustness and Fairness Zongxiong Chen Jiahui Geng Derui Zhu Herbert Woisetschlaeger Qing Li Sonja Schimmler Ruben Mayer Chunming Rong DD 58 9 0 05 May 2023
A Survey on Dataset Distillation: Approaches, Applications and Future Directions Jiahui Geng Zongxiong Chen Yuandou Wang Herbert Woisetschlaeger Sonja Schimmler Ruben Mayer Zhiming Zhao Chunming Rong DD 120 26 0 03 May 2023
Data Distillation: A Survey Noveen Sachdeva Julian McAuley DD 83 76 0 11 Jan 2023
Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory Justin Cui Ruochen Wang Si Si Cho-Jui Hsieh DD 90 137 0 19 Nov 2022
Dataset Distillation using Neural Feature Regression Yongchao Zhou E. Nezhadarya Jimmy Ba DD FedML 75 157 0 01 Jun 2022
Dataset Distillation by Matching Training Trajectories George Cazenavette Tongzhou Wang Antonio Torralba Alexei A. Efros Jun-Yan Zhu FedML DD 172 388 0 22 Mar 2022
Dataset Condensation with Contrastive Signals Saehyung Lee Sanghyuk Chun Sangwon Jung Sangdoo Yun Sung-Hoon Yoon DD 44 99 0 07 Feb 2022
Dataset Condensation with Distribution Matching Bo Zhao Hakan Bilen DD 75 304 0 08 Oct 2021
Dataset Distillation with Infinitely Wide Convolutional Networks Timothy Nguyen Roman Novak Lechao Xiao Jaehoon Lee DD 90 234 0 27 Jul 2021
Dataset Condensation with Differentiable Siamese Augmentation Bo Zhao Hakan Bilen DD 260 300 0 16 Feb 2021
Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen Zhourung Chen Jaehoon Lee DD 129 244 0 30 Oct 2020
Dataset Condensation with Gradient Matching Bo Zhao Konda Reddy Mopuri Hakan Bilen DD 113 497 0 10 Jun 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 213 1,842 0 03 Mar 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 56 404 0 26 Feb 2020
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 81 987 0 29 Nov 2019
Soft-Label Dataset Distillation and Text Dataset Distillation Ilia Sucholutsky Matthias Schonlau DD 129 135 0 06 Oct 2019
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations Eric Wong Frank R. Schmidt J. Zico Kolter AAML 71 211 0 21 Feb 2019
Certified Adversarial Robustness via Randomized Smoothing Jeremy M. Cohen Elan Rosenfeld J. Zico Kolter AAML 147 2,038 0 08 Feb 2019
Dataset Distillation Tongzhou Wang Jun-Yan Zhu Antonio Torralba Alexei A. Efros DD 78 295 0 27 Nov 2018
Robustness via curvature regularization, and vice versa Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi J. Uesato P. Frossard AAML 72 319 0 23 Nov 2018
With Friends Like These, Who Needs Adversaries? Saumya Jetley Nicholas A. Lord Philip Torr AAML 51 70 0 11 Jul 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 216 3,185 0 01 Feb 2018
Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients A. Ross Finale Doshi-Velez AAML 147 682 0 26 Nov 2017
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models Pin-Yu Chen Huan Zhang Yash Sharma Jinfeng Yi Cho-Jui Hsieh AAML 78 1,879 0 14 Aug 2017
Houdini: Fooling Deep Structured Prediction Models Moustapha Cissé Yossi Adi Natalia Neverova Joseph Keshet AAML 48 272 0 17 Jul 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 304 12,063 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 177 2,725 0 19 May 2017
Parseval Networks: Improving Robustness to Adversarial Examples Moustapha Cissé Piotr Bojanowski Edouard Grave Yann N. Dauphin Nicolas Usunier AAML 136 807 0 28 Apr 2017
Simple Black-Box Adversarial Perturbations for Deep Networks Nina Narodytska S. Kasiviswanathan AAML 67 239 0 19 Dec 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 261 8,552 0 16 Aug 2016
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 540 5,897 0 08 Jul 2016
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples Nicolas Papernot Patrick McDaniel Ian Goodfellow SILM AAML 112 1,740 0 24 May 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 193,878 0 10 Dec 2015
The Limitations of Deep Learning in Adversarial Settings Nicolas Papernot Patrick McDaniel S. Jha Matt Fredrikson Z. Berkay Celik A. Swami AAML 102 3,960 0 24 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 148 4,895 0 14 Nov 2015
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks Nicolas Papernot Patrick McDaniel Xi Wu S. Jha A. Swami AAML 92 3,072 0 14 Nov 2015
Distributional Smoothing with Virtual Adversarial Training Takeru Miyato S. Maeda Masanori Koyama Ken Nakae S. Ishii 89 458 0 02 Jul 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 274 19,049 0 20 Dec 2014