Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection

6 June 2024

Le Li

Papers citing "Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection"

20 / 20 papers shown

Title
Robust Models are less Over-Confident Julia Grabinski Paul Gavrikov J. Keuper Margret Keuper AAML 48 24 0 12 Oct 2022
SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness Jindong Gu Hengshuang Zhao Volker Tresp Philip Torr AAML 45 76 0 25 Jul 2022
Robustness and Accuracy Could Be Reconcilable by (Proper) Definition Tianyu Pang Min Lin Xiao Yang Junyi Zhu Shuicheng Yan 89 122 0 21 Feb 2022
Label Smoothing and Adversarial Robustness Chaohao Fu Hongbin Chen Na Ruan Weijia Jia AAML 33 12 0 17 Sep 2020
Overfitting in adversarially robust deep learning Leslie Rice Eric Wong Zico Kolter 94 800 0 26 Feb 2020
Boosting Adversarial Training with Hypersphere Embedding Tianyu Pang Xiao Yang Yinpeng Dong Kun Xu Jun Zhu Hang Su AAML 68 156 0 20 Feb 2020
Label Smoothing and Logit Squeezing: A Replacement for Adversarial Training? Ali Shafahi Amin Ghiasi Furong Huang Tom Goldstein AAML 59 41 0 25 Oct 2019
Adversarial Examples Are Not Bugs, They Are Features Andrew Ilyas Shibani Santurkar Dimitris Tsipras Logan Engstrom Brandon Tran Aleksander Madry SILM 89 1,837 0 06 May 2019
Adversarial Training for Free! Ali Shafahi Mahyar Najibi Amin Ghiasi Zheng Xu John P. Dickerson Christoph Studer L. Davis Gavin Taylor Tom Goldstein AAML 125 1,245 0 29 Apr 2019
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein Maksym Andriushchenko Julian Bitterwolf OODD 165 558 0 13 Dec 2018
Adversarial Attacks and Defences: A Survey Anirban Chakraborty Manaar Alam Vishal Dey Anupam Chattopadhyay Debdeep Mukhopadhyay AAML OOD 65 679 0 28 Sep 2018
Don't Use Large Mini-Batches, Use Local SGD Tao R. Lin Sebastian U. Stich Kumar Kshitij Patel Martin Jaggi 111 433 0 22 Aug 2018
Evaluating and Understanding the Robustness of Adversarial Logit Pairing Logan Engstrom Andrew Ilyas Anish Athalye AAML 55 141 0 26 Jul 2018
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning Battista Biggio Fabio Roli AAML 116 1,409 0 08 Dec 2017
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 285 12,060 0 19 Jun 2017
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 417 2,935 0 15 Sep 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 328 7,980 0 23 May 2016
Identity Mappings in Deep Residual Networks Kaiming He Xinming Zhang Shaoqing Ren Jian Sun 348 10,180 0 16 Mar 2016
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 855 27,303 0 02 Dec 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 252 19,045 0 20 Dec 2014