XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance

22 June 2023

Papers citing "XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance"

25 / 25 papers shown

Title
Minimizing False-Positive Attributions in Explanations of Non-Linear Models Anders Gjølbye Stefan Haufe Lars Kai Hansen 194 0 0 16 May 2025
Explaining the Impact of Training on Vision Models via Activation Clustering Ahcène Boubekki Samuel G. Fadel Sebastian Mair 232 0 0 29 Nov 2024
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables Rick Wilming Leo Kieslich Benedict Clark Stefan Haufe 45 10 0 02 Jun 2023
OpenXAI: Towards a Transparent Evaluation of Model Explanations Chirag Agarwal Dan Ley Satyapriya Krishna Eshika Saxena Martin Pawelczyk Nari Johnson Isha Puri Marinka Zitnik Himabindu Lakkaraju XAI 69 145 0 22 Jun 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 52 23 0 22 Feb 2022
Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond Anna Hedström Leander Weber Dilyara Bareeva Daniel G. Krakowczyk Franz Motzkus Wojciech Samek Sebastian Lapuschkin Marina M.-C. Höhne XAI ELM 44 175 0 14 Feb 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI Meike Nauta Jan Trienes Shreyasi Pathak Elisa Nguyen Michelle Peters Yasmin Schmitt Jorg Schlotterer M. V. Keulen C. Seifert ELM XAI 89 409 0 20 Jan 2022
Scrutinizing XAI using linear ground-truth data with suppressor variables Rick Wilming Céline Budding K. Müller Stefan Haufe FAtt 36 26 0 14 Nov 2021
PASS: An ImageNet replacement for self-supervised pretraining without humans Yuki M. Asano Christian Rupprecht Andrew Zisserman Andrea Vedaldi VLM SSL 77 58 0 27 Sep 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 80 135 0 27 Apr 2021
The Risks of Invariant Risk Minimization Elan Rosenfeld Pradeep Ravikumar Andrej Risteski OOD 76 312 0 12 Oct 2020
Captum: A unified and generic model interpretability library for PyTorch Narine Kokhlikyan Vivek Miglani Miguel Martin Edward Wang B. Alsallakh ... Alexander Melnikov Natalia Kliushkina Carlos Araya Siqi Yan Orion Reblitz-Richardson FAtt 133 841 0 16 Sep 2020
Large image datasets: A pyrrhic win for computer vision? Vinay Uday Prabhu Abeba Birhane 65 366 0 24 Jun 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 60 157 0 16 Mar 2020
iNNvestigate neural networks! Maximilian Alber Sebastian Lapuschkin P. Seegerer Miriam Hagele Kristof T. Schütt G. Montavon Wojciech Samek K. Müller Sven Dähne Pieter-Jan Kindermans 59 348 0 13 Aug 2018
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,906 0 22 May 2017
Learning how to explain neural networks: PatternNet and PatternAttribution Pieter-Jan Kindermans Kristof T. Schütt Maximilian Alber K. Müller D. Erhan Been Kim Sven Dähne XAI FAtt 73 339 0 16 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 198 3,873 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 297 20,023 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,976 0 16 Feb 2016
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition G. Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus-Robert Muller FAtt 60 737 0 08 Dec 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,672 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,295 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,882 0 12 Nov 2013