Feature Accentuation: Revealing 'What' Features Respond to in Natural Images

15 February 2024

Papers citing "Feature Accentuation: Revealing 'What' Features Respond to in Natural Images"

6 / 6 papers shown

Title
Universal Sparse Autoencoders: Interpretable Cross-Model Concept Alignment Harrish Thasarathan Julian Forsyth Thomas Fel M. Kowal Konstantinos G. Derpanis 111 7 0 06 Feb 2025
Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations Amin Ghiasi Hamid Kazemi Steven Reich Chen Zhu Micah Goldblum Tom Goldstein 42 15 0 31 Jan 2022
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 0 06 Dec 2021
Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis Thomas Fel Rémi Cadène Mathieu Chalvidal Matthieu Cord David Vigouroux Thomas Serre MLAU FAtt AAML 114 58 0 07 Nov 2021
Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AI Alon Jacovi Ana Marasović Tim Miller Yoav Goldberg 252 426 0 15 Oct 2020
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 251 3,683 0 28 Feb 2017