On the Benefits of Models with Perceptually-Aligned Gradients

4 May 2020

Papers citing "On the Benefits of Models with Perceptually-Aligned Gradients"

7 / 7 papers shown

Title
Theoretical Understanding of Learning from Adversarial Perturbations Soichiro Kumano Hiroshi Kera Toshihiko Yamasaki AAML 31 1 0 16 Feb 2024
Which Models have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness Suraj Srinivas Sebastian Bordt Hima Lakkaraju AAML 25 11 0 30 May 2023
Exploring the Connection between Robust and Generative Models Senad Beadini I. Masi AAML 24 1 0 08 Apr 2023
Enhancing Diffusion-Based Image Synthesis with Robust Classifier Guidance Bahjat Kawar Roy Ganz Michael Elad DiffM 23 38 0 18 Aug 2022
Non-generative Generalized Zero-shot Learning via Task-correlated Disentanglement and Controllable Samples Synthesis Yaogong Feng Xiaowen Huang Pengbo Yang Jian Yu Jitao Sang DiffM 24 29 0 10 Mar 2022
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 30 25 0 20 Mar 2021
Do Adversarially Robust ImageNet Models Transfer Better? Hadi Salman Andrew Ilyas Logan Engstrom Ashish Kapoor A. Madry 34 416 0 16 Jul 2020