Achieving Adversarial Robustness via Sparsity

Achieving Adversarial Robustness via Sparsity

11 September 2020

Papers citing "Achieving Adversarial Robustness via Sparsity"

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 38 0 0 07 Apr 2025
Dynamic Sparse Training versus Dense Training: The Unexpected Winner in Image Corruption Robustness Boqian Wu Q. Xiao Shunxin Wang N. Strisciuglio Mykola Pechenizkiy M. V. Keulen Decebal Constantin Mocanu Elena Mocanu OOD 3DH 57 0 0 03 Oct 2024
Robust low-rank training via approximate orthonormal constraints Dayana Savostianova Emanuele Zangrando Gianluca Ceruti Francesco Tudisco 24 9 0 02 Jun 2023
An Adversarial Robustness Perspective on the Topology of Neural Networks Morgane Goibert Thomas Ricatte Elvis Dohmatob AAML 11 2 0 04 Nov 2022
Finding Dynamics Preserving Adversarial Winning Tickets Xupeng Shi Pengfei Zheng Adam Ding Yuan Gao Weizhong Zhang AAML 21 1 0 14 Feb 2022
Neural network relief: a pruning algorithm based on neural activity Aleksandr Dekhovich David Tax M. Sluiter Miguel A. Bessa 46 10 0 22 Sep 2021
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution Robustness James Diffenderfer Brian Bartoldson Shreya Chaganti Jize Zhang B. Kailkhura OOD 31 69 0 16 Jun 2021
Generating Natural Language Adversarial Examples M. Alzantot Yash Sharma Ahmed Elgohary Bo-Jhang Ho Mani B. Srivastava Kai-Wei Chang AAML 245 915 0 21 Apr 2018
Adversarial Machine Learning at Scale Alexey Kurakin Ian Goodfellow Samy Bengio AAML 293 3,110 0 04 Nov 2016
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 308 2,890 0 15 Sep 2016