Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks

26 February 2020

Papers citing "Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks"

6 / 6 papers shown

Title
Training Image Derivatives: Increased Accuracy and Universal Robustness V. Avrutskiy 36 0 0 21 Oct 2023
Strong inductive biases provably prevent harmless interpolation Michael Aerni Marco Milanta Konstantin Donhauser Fanny Yang 30 9 0 18 Jan 2023
Robust Perception through Equivariance Chengzhi Mao Lingyu Zhang Abhishek Joshi Junfeng Yang Hongya Wang Carl Vondrick BDL AAML 24 7 0 12 Dec 2022
On the Limitations of Stochastic Pre-processing Defenses Yue Gao Ilia Shumailov Kassem Fawaz Nicolas Papernot AAML SILM 34 30 0 19 Jun 2022
A General Theory of Equivariant CNNs on Homogeneous Spaces Taco S. Cohen Mario Geiger Maurice Weiler MLT AI4CE 151 308 0 05 Nov 2018
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks Guy Katz Clark W. Barrett D. Dill Kyle D. Julian Mykel Kochenderfer AAML 226 1,835 0 03 Feb 2017