Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions

16 March 2016

Papers citing "Suppressing the Unusual: towards Robust CNNs using Symmetric Activation Functions"

7 / 7 papers shown

Title
How important are activation functions in regression and classification? A survey, performance comparison, and future directions Ameya Dilip Jagtap George Karniadakis AI4CE 42 71 0 06 Sep 2022
Parameterizing Activation Functions for Adversarial Robustness Sihui Dai Saeed Mahloujifar Prateek Mittal AAML 47 32 0 11 Oct 2021
SPLASH: Learnable Activation Functions for Improving Accuracy and Adversarial Robustness Mohammadamin Tavakoli Forest Agostinelli Pierre Baldi AAML FAtt 36 39 0 16 Jun 2020
Motivating the Rules of the Game for Adversarial Example Research Justin Gilmer Ryan P. Adams Ian Goodfellow David G. Andersen George E. Dahl AAML 50 226 0 18 Jul 2018
With Friends Like These, Who Needs Adversaries? Saumya Jetley Nicholas A. Lord Philip Torr AAML 21 70 0 11 Jul 2018
Blocking Transferability of Adversarial Examples in Black-Box Learning Systems Hossein Hosseini Yize Chen Sreeram Kannan Baosen Zhang Radha Poovendran AAML 30 106 0 13 Mar 2017
Robustness of classifiers: from adversarial to random noise Alhussein Fawzi Seyed-Mohsen Moosavi-Dezfooli P. Frossard AAML 16 367 0 31 Aug 2016