Explaining Image Classifiers by Counterfactual Generation

20 July 2018

Papers citing "Explaining Image Classifiers by Counterfactual Generation"

35 / 85 papers shown

Title
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications Dongqi Han Zhiliang Wang Wenqi Chen Ying Zhong Su Wang Han Zhang Jiahai Yang Xingang Shi Xia Yin AAML 24 76 0 23 Sep 2021
Deriving Explanation of Deep Visual Saliency Models S. Malladi J. Mukhopadhyay M. Larabi S. Chaudhury FAtt XAI 18 0 0 08 Sep 2021
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey Richard Dazeley Peter Vamplew Francisco Cruz 32 60 0 20 Aug 2021
Temporal Dependencies in Feature Importance for Time Series Predictions Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi M. Volkovs OOD AI4TS 36 24 0 29 Jul 2021
Contrastive Counterfactual Visual Explanations With Overdetermination Adam White K. Ngan James Phelan Saman Sadeghi Afgeh Kevin Ryan C. Reyes-Aldasoro Artur Garcez 21 8 0 28 Jun 2021
Towards Robust Classification Model by Counterfactual and Invariant Data Generation C. Chang George Adam Anna Goldenberg OOD CML 27 32 0 02 Jun 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase Harry Xie Joey Tianyi Zhou OODD LRM FAtt 29 91 0 01 Jun 2021
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis Samuele Poppi Marcella Cornia Lorenzo Baraldi Rita Cucchiara FAtt 131 33 0 20 Apr 2021
Explaining the Road Not Taken Hua Shen Ting-Hao 'Kenneth' Huang FAtt XAI 32 9 0 27 Mar 2021
Robust Models Are More Interpretable Because Attributions Look Normal Zifan Wang Matt Fredrikson Anupam Datta OOD FAtt 35 25 0 20 Mar 2021
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations Pau Rodríguez López Massimo Caccia Alexandre Lacoste L. Zamparo I. Laradji Laurent Charlin David Vazquez AAML 37 55 0 18 Mar 2021
BBAM: Bounding Box Attribution Map for Weakly Supervised Semantic and Instance Segmentation Jungbeom Lee Jihun Yi Chaehun Shin Sungroh Yoon ISeg 24 172 0 16 Mar 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 67 100 0 11 Jan 2021
Interpretability and Explainability: A Machine Learning Zoo Mini-tour Ricards Marcinkevics Julia E. Vogt XAI 28 119 0 03 Dec 2020
Understanding Failures of Deep Networks via Robust Feature Extraction Sahil Singla Besmira Nushi S. Shah Ece Kamar Eric Horvitz FAtt 28 83 0 03 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 53 243 0 21 Nov 2020
Feature Removal Is a Unifying Principle for Model Explanation Methods Ian Covert Scott M. Lundberg Su-In Lee FAtt 33 33 0 06 Nov 2020
Interpretation of NLP models through input marginalization Siwon Kim Jihun Yi Eunji Kim Sungroh Yoon MILM FAtt 30 58 0 27 Oct 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 25 9 0 29 Sep 2020
Counterfactual Explanation and Causal Inference in Service of Robustness in Robot Control Simón C. Smith S. Ramamoorthy 26 13 0 18 Sep 2020
iCaps: An Interpretable Classifier via Disentangled Capsule Networks Dahuin Jung Jonghyun Lee Jihun Yi Sungroh Yoon 33 12 0 20 Aug 2020
Counterfactual Explanation Based on Gradual Construction for Deep Networks Hong G Jung Sin-Han Kang Hee-Dong Kim Dong-Ok Won Seong-Whan Lee OOD FAtt 25 22 0 05 Aug 2020
A simple defense against adversarial attacks on heatmap explanations Laura Rieger Lars Kai Hansen FAtt AAML 33 37 0 13 Jul 2020
Scientific Discovery by Generating Counterfactuals using Image Translation Arunachalam Narayanaswamy Subhashini Venugopalan D. Webster L. Peng G. Corrado ... Abigail E. Huang Siva Balasubramanian Michael P. Brenner Phil Q. Nelson A. Varadarajan DiffM MedIm 30 20 0 10 Jul 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
Getting a CLUE: A Method for Explaining Uncertainty Estimates Javier Antorán Umang Bhatt T. Adel Adrian Weller José Miguel Hernández-Lobato UQCV BDL 50 112 0 11 Jun 2020
Evaluating and Aggregating Feature-based Model Explanations Umang Bhatt Adrian Weller J. M. F. Moura XAI 33 218 0 01 May 2020
Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis David Major Dimitrios Lenis M. Wimmer Gert Sluiter Astrid Berg Katja Bühler FAtt 27 12 0 03 Apr 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability Maximilian Augustin Alexander Meinke Matthias Hein OOD 75 99 0 20 Mar 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 56 14 0 05 Mar 2020
Explaining Visual Models by Causal Attribution Álvaro Parafita Jordi Vitrià CML FAtt 62 35 0 19 Sep 2019
Grid Saliency for Context Explanations of Semantic Segmentation Lukas Hoyer Mauricio Muñoz P. Katiyar Anna Khoreva Volker Fischer FAtt 25 48 0 30 Jul 2019
Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias Emily L. Denton B. Hutchinson Margaret Mitchell Timnit Gebru Andrew Zaldivar CVBM 21 130 0 14 Jun 2019
On the (In)fidelity and Sensitivity for Explanations Chih-Kuan Yeh Cheng-Yu Hsieh A. Suggala David I. Inouye Pradeep Ravikumar FAtt 39 449 0 27 Jan 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,167 0 06 Jun 2015