To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression

29 September 2018

Papers citing "To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression"

10 / 10 papers shown

Title
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models Yoojin Jung Byung Cheol Song AAML VLM MQ 41 0 0 07 Apr 2025
QEBVerif: Quantization Error Bound Verification of Neural Networks Yedi Zhang Fu Song Jun Sun MQ 26 11 0 06 Dec 2022
Attacking Compressed Vision Transformers Swapnil Parekh Devansh Shah Pratyush Shukla AAML 24 1 0 28 Sep 2022
Learning Robust and Lightweight Model through Separable Structured Transformations Xian Wei Yanhui Huang Yang Xu Mingsong Chen Hai Lan Yuanxiang Li Zhongfeng Wang Xuan Tang OOD 24 0 0 27 Dec 2021
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy Lucas Liebenwein Cenk Baykal Brandon Carter David K Gifford Daniela Rus AAML 42 71 0 04 Mar 2021
Robustness and Transferability of Universal Attacks on Compressed Models Alberto G. Matachana Kenneth T. Co Luis Muñoz-González David Martínez Emil C. Lupu AAML 29 10 0 10 Dec 2020
Towards Practical Lottery Ticket Hypothesis for Adversarial Training Bai Li Shiqi Wang Yunhan Jia Yantao Lu Zhenyu Zhong Lawrence Carin Suman Jana AAML 31 14 0 06 Mar 2020
Impact of Low-bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks Rémi Bernhard Pierre-Alain Moëllic J. Dutertre AAML MQ 26 18 0 27 Sep 2019
Adversarially Robust Distillation Micah Goldblum Liam H. Fowl S. Feizi Tom Goldstein AAML 15 201 0 23 May 2019
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 350 5,849 0 08 Jul 2016