The (Un)reliability of saliency methods

The (Un)reliability of saliency methods

2 November 2017

Pieter-Jan Kindermans

Maximilian Alber

Kristof T. Schütt

Papers citing "The (Un)reliability of saliency methods"

19 / 119 papers shown

Title
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference Sebastian Gehrmann Hendrik Strobelt Robert Krüger Hanspeter Pfister Alexander M. Rush HAI 15 57 0 24 Jul 2019
Incorporating Priors with Feature Attribution on Text Classification Frederick Liu Besim Avci FAtt FaML 28 120 0 19 Jun 2019
Adversarial Robustness as a Prior for Learned Representations Logan Engstrom Andrew Ilyas Shibani Santurkar Dimitris Tsipras Brandon Tran A. Madry OOD AAML 11 63 0 03 Jun 2019
Certifiably Robust Interpretation in Deep Learning Alexander Levine Sahil Singla S. Feizi FAtt AAML 17 63 0 28 May 2019
What Do Adversarially Robust Models Look At? Takahiro Itazuri Yoshihiro Fukuhara Hirokatsu Kataoka Shigeo Morishima 16 5 0 19 May 2019
Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms Mkhuseli Ngxande J. Tapamo Michael G. Burke 17 12 0 23 Apr 2019
Software and application patterns for explanation methods Maximilian Alber 25 11 0 09 Apr 2019
Regression Concept Vectors for Bidirectional Explanations in Histopathology Mara Graziani Vincent Andrearczyk Henning Muller 31 78 0 09 Apr 2019
Explaining Anomalies Detected by Autoencoders Using SHAP Liat Antwarg Ronnie Mindlin Miller Bracha Shapira L. Rokach FAtt TDI 11 86 0 06 Mar 2019
Interpretable Deep Learning under Fire Xinyang Zhang Ningfei Wang Hua Shen S. Ji Xiapu Luo Ting Wang AAML AI4CE 13 168 0 03 Dec 2018
An Overview of Computational Approaches for Interpretation Analysis Philipp Blandfort Jörn Hees D. Patton 21 2 0 09 Nov 2018
What made you do this? Understanding black-box decisions with sufficient input subsets Brandon Carter Jonas W. Mueller Siddhartha Jain David K Gifford FAtt 35 77 0 09 Oct 2018
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 35 1,926 0 08 Oct 2018
xGEMs: Generating Examplars to Explain Black-Box Models Shalmali Joshi Oluwasanmi Koyejo Been Kim Joydeep Ghosh MLAU 22 40 0 22 Jun 2018
On the Robustness of Interpretability Methods David Alvarez-Melis Tommi Jaakkola 19 521 0 21 Jun 2018
Towards Robust Interpretability with Self-Explaining Neural Networks David Alvarez-Melis Tommi Jaakkola MILM XAI 15 932 0 20 Jun 2018
Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges Gabrielle Ras Marcel van Gerven W. Haselager XAI 17 217 0 20 Mar 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 38 1,789 0 30 Nov 2017
Deep Learning Techniques for Music Generation -- A Survey Jean-Pierre Briot Gaëtan Hadjeres F. Pachet MGen 32 297 0 05 Sep 2017