ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations

9 June 2024

R. Venkatesh Babu

Papers citing "ProFeAT: Projected Feature Adversarial Training for Self-Supervised Learning of Robust Representations"

36 / 36 papers shown

Title
Investigating the Benefits of Projection Head for Representation Learning Yihao Xue Eric Gan Jiayi Ni Siddharth Joshi Baharan Mirzasoleiman 86 14 0 18 Mar 2024
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness Chaoning Zhang Kang Zhang Chenshuang Zhang Axi Niu Jiu Feng Chang D. Yoo In So Kweon SSL 88 25 0 22 Jul 2022
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning Florian Bordes Randall Balestriero Q. Garrido Adrien Bardes Pascal Vincent 102 24 0 27 Jun 2022
Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distribution Ananya Kumar Aditi Raghunathan Robbie Jones Tengyu Ma Percy Liang OODD 132 685 0 21 Feb 2022
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation K. Navaneet Soroush Abbasi Koohpayegani Ajinkya Tejankar Hamed Pirsiavash 51 20 0 13 Jan 2022
When Does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? Lijie Fan Sijia Liu Pin-Yu Chen Gaoyuan Zhang Chuang Gan AAML VLM 90 124 0 01 Nov 2021
DisCo: Remedy Self-supervised Learning on Lightweight Models with Distilled Contrastive Learning Yuting Gao Jia-Xin Zhuang Xiaowei Guo Hao Cheng Xing Sun Ke Li Feiyue Huang 101 40 0 19 Apr 2021
Barlow Twins: Self-Supervised Learning via Redundancy Reduction Jure Zbontar Li Jing Ishan Misra Yann LeCun Stéphane Deny SSL 347 2,368 0 04 Mar 2021
Exploring Simple Siamese Representation Learning Xinlei Chen Kaiming He SSL 258 4,076 0 20 Nov 2020
Robust Pre-Training by Adversarial Contrastive Learning Ziyu Jiang Tianlong Chen Ting-Li Chen Zhangyang Wang 106 234 0 26 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 339 705 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 59 331 0 07 Oct 2020
Bag of Tricks for Adversarial Training Tianyu Pang Xiao Yang Yinpeng Dong Hang Su Jun Zhu AAML 82 269 0 01 Oct 2020
Bootstrap your own latent: A new approach to self-supervised Learning Jean-Bastien Grill Florian Strub Florent Altché Corentin Tallec Pierre Harvey Richemond ... M. G. Azar Bilal Piot Koray Kavukcuoglu Rémi Munos Michal Valko SSL 423 6,849 0 13 Jun 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks Francesco Croce Matthias Hein AAML 241 1,861 0 03 Mar 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 124 809 0 26 Feb 2020
On Adaptive Attacks to Adversarial Example Defenses Florian Tramèr Nicholas Carlini Wieland Brendel Aleksander Madry AAML 285 839 0 19 Feb 2020
A Simple Framework for Contrastive Learning of Visual Representations Ting-Li Chen Simon Kornblith Mohammad Norouzi Geoffrey E. Hinton SSL 395 18,897 0 13 Feb 2020
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations Florian Tramèr Jens Behrmann Nicholas Carlini Nicolas Papernot J. Jacobsen AAML SILM 60 93 0 11 Feb 2020
Square Attack: a query-efficient black-box adversarial attack via random search Maksym Andriushchenko Francesco Croce Nicolas Flammarion Matthias Hein AAML 103 992 0 29 Nov 2019
Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He Haoqi Fan Yuxin Wu Saining Xie Ross B. Girshick SSL 216 12,136 0 13 Nov 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 95 1,845 0 06 May 2019
On Evaluating Adversarial Robustness Nicholas Carlini Anish Athalye Nicolas Papernot Wieland Brendel Jonas Rauber Dimitris Tsipras Ian Goodfellow Aleksander Madry Alexey Kurakin ELM AAML 115 905 0 18 Feb 2019
AutoAugment: Learning Augmentation Policies from Data E. D. Cubuk Barret Zoph Dandelion Mané Vijay Vasudevan Quoc V. Le 135 1,775 0 24 May 2018
Adversarially Robust Generalization Requires More Data Ludwig Schmidt Shibani Santurkar Dimitris Tsipras Kunal Talwar Aleksander Madry OOD AAML 164 797 0 30 Apr 2018
Unsupervised Representation Learning by Predicting Image Rotations Spyros Gidaris Praveer Singh N. Komodakis OOD SSL DRL 267 3,303 0 21 Mar 2018
Stochastic Activation Pruning for Robust Adversarial Defense Guneet Singh Dhillon Kamyar Azizzadenesheli Zachary Chase Lipton Jeremy Bernstein Jean Kossaifi Aran Khanna Anima Anandkumar AAML 84 548 0 05 Mar 2018
Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples Anish Athalye Nicholas Carlini D. Wagner AAML 249 3,195 0 01 Feb 2018
Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality Xingjun Ma Yue Liu Yisen Wang S. Erfani S. Wijewickrema Grant Schoenebeck Basel Alomair Michael E. Houle James Bailey AAML 120 742 0 08 Jan 2018
Mitigating Adversarial Effects Through Randomization Cihang Xie Jianyu Wang Zhishuai Zhang Zhou Ren Alan Yuille AAML 125 1,063 0 06 Nov 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 319 12,151 0 19 Jun 2017
Ensemble Adversarial Training: Attacks and Defenses Florian Tramèr Alexey Kurakin Nicolas Papernot Ian Goodfellow Dan Boneh Patrick McDaniel AAML 185 2,731 0 19 May 2017
Wide Residual Networks Sergey Zagoruyko N. Komodakis 362 8,005 0 23 May 2016
Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles M. Noroozi Paolo Favaro SSL 180 2,986 0 30 Mar 2016
Practical Black-Box Attacks against Machine Learning Nicolas Papernot Patrick McDaniel Ian Goodfellow S. Jha Z. Berkay Celik A. Swami MLAU AAML 85 3,685 0 08 Feb 2016
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 293 14,978 1 21 Dec 2013