"Is your explanation stable?": A Robustness Evaluation Framework for
Feature Attribution

"Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution

5 September 2022

Xuhong Zhang

Yuwen Pu

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Papers citing ""Is your explanation stable?": A Robustness Evaluation Framework for Feature Attribution"

17 / 17 papers shown

Title
Probabilistic Stability Guarantees for Feature Attributions Helen Jin Anton Xue Weiqiu You Surbhi Goel Eric Wong 119 1 0 18 Apr 2025
NeuronFair: Interpretable White-Box Fairness Testing through Biased Neuron Identification Haibin Zheng Zhiqing Chen Tianyu Du Xuhong Zhang Yao Cheng S. Ji Jingyi Wang Yue Yu Jinyin Chen 70 53 0 25 Dec 2021
Group-CAM: Group Score-Weighted Visual Explanations for Deep Convolutional Networks Qing-Long Zhang Lu Rao Yubin Yang 47 58 0 25 Mar 2021
Interpreting Super-Resolution Networks with Local Attribution Maps Jinjin Gu Chao Dong FAtt SupR 60 220 0 22 Nov 2020
Black-box Explanation of Object Detectors via Saliency Maps Vitali Petsiuk R. Jain Varun Manjunatha Vlad I. Morariu Ashutosh Mehra Vicente Ordonez Kate Saenko FAtt 58 124 0 05 Jun 2020
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks Mehdi Neshat Zifan Wang Bradley Alexander Fan Yang Zijian Zhang Sirui Ding Markus Wagner Xia Hu FAtt 93 1,074 0 03 Oct 2019
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 334 0 19 Jun 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,970 0 08 Oct 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,171 0 19 Jun 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 101 687 0 02 Nov 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
The Cityscapes Dataset for Semantic Urban Scene Understanding Marius Cordts Mohamed Omran Sebastian Ramos Timo Rehfeld Markus Enzweiler Rodrigo Benenson Uwe Franke Stefan Roth Bernt Schiele 1.1K 11,641 0 06 Apr 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,326 0 14 Dec 2015
Sequence to Sequence Learning with Neural Networks Ilya Sutskever Oriol Vinyals Quoc V. Le AIMat 437 20,584 0 10 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,308 0 20 Dec 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 137 1,104 0 06 Dec 2009