Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization

17 November 2015

Papers citing "Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization"

8 / 8 papers shown

Title
Risk Analysis and Design Against Adversarial Actions M. Campi A. Carè Luis G. Crespo S. Garatti Federico A. Ramponi AAML 177 0 0 02 May 2025
Decoding Game: On Minimax Optimality of Heuristic Text Generation Strategies Sijin Chen Omar Hagrass Jason M. Klusowski 32 3 0 04 Oct 2024
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 39 55 0 05 Dec 2021
Generative Dynamic Patch Attack Xiang Li Shihao Ji AAML 30 22 0 08 Nov 2021
Simple Post-Training Robustness Using Test Time Augmentations and Random Forest Gilad Cohen Raja Giryes AAML 40 4 0 16 Sep 2021
Mitigating Gradient-based Adversarial Attacks via Denoising and Compression Rehana Mahfuz R. Sahay Aly El Gamal AAML 14 3 0 03 Apr 2021
Adversarial Training against Location-Optimized Adversarial Patches Sukrut Rao David Stutz Bernt Schiele AAML 19 91 0 05 May 2020
Towards Evaluating the Robustness of Neural Networks Nicholas Carlini D. Wagner OOD AAML 53 8,448 0 16 Aug 2016