Adversarially Robust Distillation

23 May 2019

Papers citing "Adversarially Robust Distillation"

35 / 35 papers shown

Title
Long-tailed Adversarial Training with Self-Distillation Seungju Cho Hongsin Lee Changick Kim AAML TTA 415 0 0 09 Mar 2025
Adversarial Prompt Distillation for Vision-Language Models Lin Luo Xin Wang Bojia Zi Shihao Zhao Xingjun Ma Yu-Gang Jiang AAML VLM 116 3 0 22 Nov 2024
Is Adversarial Training with Compressed Datasets Effective? Tong Chen Raghavendra Selvan AAML 109 0 0 08 Feb 2024
Adversarial attacks on Copyright Detection Systems Parsa Saadatpanah Ali Shafahi Tom Goldstein AAML 42 33 0 17 Jun 2019
Towards Compact and Robust Deep Neural Networks Vikash Sehwag Shiqi Wang Prateek Mittal Suman Jana AAML 59 40 0 14 Jun 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle Dinghuai Zhang Tianyuan Zhang Yiping Lu Zhanxing Zhu Bin Dong AAML 96 361 0 02 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
Defensive Quantization: When Efficiency Meets Robustness Ji Lin Chuang Gan Song Han MQ 69 203 0 17 Apr 2019
Theoretically Principled Trade-off between Robustness and Accuracy Hongyang R. Zhang Yaodong Yu Jiantao Jiao Eric Xing L. Ghaoui Michael I. Jordan 127 2,542 0 24 Jan 2019
Image Super-Resolution as a Defense Against Adversarial Attacks Aamir Mustafa Salman H. Khan Munawar Hayat Jianbing Shen Ling Shao AAML SupR 67 175 0 07 Jan 2019
Feature Denoising for Improving Adversarial Robustness Cihang Xie Yuxin Wu Laurens van der Maaten Alan Yuille Kaiming He 102 908 0 09 Dec 2018
Knowledge Distillation with Feature Maps for Image Classification Wei-Chun Chen Chia-Che Chang Chien-Yu Lu Che-Rung Lee 51 35 0 03 Dec 2018
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression Yiren Zhao Ilia Shumailov Robert D. Mullins Ross J. Anderson AAML 36 43 0 29 Sep 2018
AutoAugment: Learning Augmentation Policies from Data E. D. Cubuk Barret Zoph Dandelion Mané Vijay Vasudevan Quoc V. Le 118 1,771 0 24 May 2018
Adversarial Logit Pairing Harini Kannan Alexey Kurakin Ian Goodfellow AAML 95 628 0 16 Mar 2018
Deflecting Adversarial Attacks with Pixel Deflection Aaditya (Adi) Prakash N. Moran Solomon Garber Antonella DiLillo J. Storer AAML 61 303 0 26 Jan 2018
MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler Andrew G. Howard Menglong Zhu A. Zhmoginov Liang-Chieh Chen 169 19,204 0 13 Jan 2018
Towards Deep Learning Models Resistant to Adversarial Attacks Aleksander Madry Aleksandar Makelov Ludwig Schmidt Dimitris Tsipras Adrian Vladu SILM OOD 275 12,029 0 19 Jun 2017
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning Takeru Miyato S. Maeda Masanori Koyama S. Ishii GAN 143 2,732 0 13 Apr 2017
Pruning Filters for Efficient ConvNets Hao Li Asim Kadav Igor Durdanovic H. Samet H. Graf 3DPC 188 3,693 0 31 Aug 2016
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 235 8,548 0 16 Aug 2016
Learning a Driving Simulator Eder Santana George Hotz GAN 69 226 0 03 Aug 2016
A study of the effect of JPG compression on adversarial images Gintare Karolina Dziugaite Zoubin Ghahramani Daniel M. Roy AAML 81 533 0 02 Aug 2016
Defensive Distillation is Not Robust to Adversarial Examples Nicholas Carlini D. Wagner 56 338 0 14 Jul 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 318 7,971 0 23 May 2016
Binarized Neural Networks Itay Hubara Daniel Soudry Ran El-Yaniv MQ 175 1,349 0 08 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.0K 193,426 0 10 Dec 2015
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 768 27,303 0 02 Dec 2015
Convolutional neural networks with low-rank regularization Cheng Tai Tong Xiao Yi Zhang Xiaogang Wang E. Weinan BDL 97 460 0 19 Nov 2015
Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization Uri Shaham Yutaro Yamada S. Negahban AAML 48 77 0 17 Nov 2015
DeepFool: a simple and accurate method to fool deep neural networks Seyed-Mohsen Moosavi-Dezfooli Alhussein Fawzi P. Frossard AAML 129 4,886 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 76 3,072 0 14 Nov 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 316 19,609 0 09 Mar 2015
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 239 19,017 0 20 Dec 2014
Intriguing properties of neural networks Christian Szegedy Wojciech Zaremba Ilya Sutskever Joan Bruna D. Erhan Ian Goodfellow Rob Fergus AAML 245 14,893 1 21 Dec 2013