Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks

12 January 2024

Stefan Blücher

Papers citing "Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks"

30 / 30 papers shown

Title
MambaLRP: Explaining Selective State Space Sequence Models F. Jafari G. Montavon Klaus-Robert Müller Oliver Eberle Mamba 197 11 0 11 Jun 2024
DiG-IN: Diffusion Guidance for Investigating Networks -- Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations Maximilian Augustin Yannic Neuhaus Matthias Hein DiffM 63 4 0 29 Nov 2023
Robust Infidelity: When Faithfulness Measures on Masked Language Models Are Misleading Evan Crothers H. Viktor Nathalie Japkowicz AAML 42 1 0 13 Aug 2023
Manifold Restricted Interventional Shapley Values Muhammad Faaiz Taufiq Patrick Blobaum Lenon Minorics FAtt 91 8 0 10 Jan 2023
Deletion and Insertion Tests in Regression Models Naofumi Hama Masayoshi Mase Art B. Owen 42 8 0 25 May 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 43 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 40 175 0 14 Feb 2022
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective Satyapriya Krishna Tessa Han Alex Gu Steven Wu S. Jabbari Himabindu Lakkaraju 241 193 0 03 Feb 2022
RePaint: Inpainting using Denoising Diffusion Probabilistic Models Andreas Lugmayr Martin Danelljan Andrés Romero Feng Yu Radu Timofte Luc Van Gool DiffM 328 1,396 0 24 Jan 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 78 409 0 20 Jan 2022
Toward Explainable AI for Regression Models S. Letzgus Patrick Wagner Jonas Lederer Wojciech Samek Klaus-Robert Muller G. Montavon XAI 59 63 0 21 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 48 5 0 09 Dec 2021
ResNet strikes back: An improved training procedure in timm Ross Wightman Hugo Touvron Hervé Jégou AI4TS 239 492 0 01 Oct 2021
Resisting Out-of-Distribution Data Problem in Perturbation of XAI Luyu Qiu Yi Yang Caleb Chen Cao Jing Liu Yueyuan Zheng H. Ngai J. H. Hsiao Lei Chen 39 18 0 27 Jul 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 62 63 0 18 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 78 248 0 21 Nov 2020
Fairwashing Explanations with Off-Manifold Detergent Christopher J. Anders Plamen Pasliev Ann-Kathrin Dombrowski K. Müller Pan Kessel FAtt FaML 42 97 0 20 Jul 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 77 223 0 01 May 2020
Sanity Checks for Saliency Metrics Richard J. Tomsett Daniel Harborne Supriyo Chakraborty Prudhvi K. Gurram Alun D. Preece XAI 67 169 0 29 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 66 814 0 06 Nov 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 72 330 0 19 Jun 2019
Unrestricted Permutation forces Extrapolation: Variable Importance Requires at least One More Model, or There Is No Free Variable Importance Giles Hooker L. Mentch Siyu Zhou 63 157 0 01 May 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn Sebastian Lapuschkin S. Wäldchen Alexander Binder G. Montavon Wojciech Samek K. Müller 82 1,009 0 26 Feb 2019
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 170 1,169 0 19 Jun 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 187 1,834 0 30 Nov 2017
Methods for Interpreting and Understanding Deep Neural Networks G. Montavon Wojciech Samek K. Müller FaML 276 2,257 0 24 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 811 21,760 0 22 May 2017
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 863 16,891 0 16 Feb 2016
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 453 15,861 0 12 Nov 2013
How to Explain Individual Classification Decisions D. Baehrens T. Schroeter Stefan Harmeling M. Kawanabe K. Hansen K. Müller FAtt 126 1,102 0 06 Dec 2009