A Benchmark for Interpretability Methods in Deep Neural Networks

28 June 2018

Sara Hooker

D. Erhan

Pieter-Jan Kindermans

Papers citing "A Benchmark for Interpretability Methods in Deep Neural Networks"

50 / 108 papers shown

Title
Attribution-based Explanations that Provide Recourse Cannot be Robust H. Fokkema R. D. Heide T. Erven FAtt 42 18 0 31 May 2022
Faithful Explanations for Deep Graph Models Zifan Wang Yuhang Yao Chaoran Zhang Han Zhang Youjie Kang Carlee Joe-Wong Matt Fredrikson Anupam Datta FAtt 14 2 0 24 May 2022
Necessity and Sufficiency for Explaining Text Classifiers: A Case Study in Hate Speech Detection Esma Balkir I. Nejadgholi Kathleen C. Fraser S. Kiritchenko FAtt 33 27 0 06 May 2022
Adapting and Evaluating Influence-Estimation Methods for Gradient-Boosted Decision Trees Jonathan Brophy Zayd Hammoudeh Daniel Lowd TDI 16 22 0 30 Apr 2022
Maximum Entropy Baseline for Integrated Gradients Hanxiao Tan FAtt 14 4 0 12 Apr 2022
Analyzing the Effects of Handling Data Imbalance on Learned Features from Medical Images by Looking Into the Models Ashkan Khakzar Yawei Li Yang Zhang Mirac Sanisoglu Seong Tae Kim Mina Rezaei Bernd Bischl Nassir Navab 15 0 0 04 Apr 2022
Label-Free Explainability for Unsupervised Models Jonathan Crabbé M. Schaar FAtt MILM 19 22 0 03 Mar 2022
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest Shucen Ma Jianqi Shi Yanhong Huang Shengchao Qin Zhe Hou AAML 16 4 0 25 Feb 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 6 22 0 22 Feb 2022
Don't Lie to Me! Robust and Efficient Explainability with Verified Perturbation Analysis Thomas Fel Mélanie Ducoffe David Vigouroux Rémi Cadène Mikael Capelle C. Nicodeme Thomas Serre AAML 18 41 0 15 Feb 2022
Multi-Modal Knowledge Graph Construction and Application: A Survey Xiangru Zhu Zhixu Li Xiaodan Wang Xueyao Jiang Penglei Sun Xuwu Wang Yanghua Xiao N. Yuan 26 154 0 11 Feb 2022
The impact of feature importance methods on the interpretation of defect classifiers Gopi Krishnan Rajbahadur Shaowei Wang Yasutaka Kamei Ahmed E. Hassan FAtt 6 79 0 04 Feb 2022
When less is more: Simplifying inputs aids neural network understanding R. Schirrmeister Rosanne Liu Sara Hooker T. Ball 19 5 0 14 Jan 2022
Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations Siddhant Arora Danish Pruthi Norman M. Sadeh William W. Cohen Zachary Chase Lipton Graham Neubig FAtt 30 38 0 17 Dec 2021
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction Aaron Chan Maziar Sanjabi Lambert Mathias L Tan Shaoliang Nie Xiaochang Peng Xiang Ren Hamed Firooz 36 41 0 16 Dec 2021
Evaluating saliency methods on artificial data with different background types Céline Budding Fabian Eitel K. Ritter Stefan Haufe XAI FAtt MedIm 11 5 0 09 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 58 114 0 06 Dec 2021
Improving Deep Learning Interpretability by Saliency Guided Training Aya Abdelsalam Ismail H. C. Bravo S. Feizi FAtt 16 79 0 29 Nov 2021
Human Attention in Fine-grained Classification Yao Rong Wenjia Xu Zeynep Akata Enkelejda Kasneci 26 35 0 02 Nov 2021
Double Trouble: How to not explain a text classifier's decisions using counterfactuals synthesized by masked language models? Thang M. Pham Trung H. Bui Long Mai Anh Totti Nguyen 21 7 0 22 Oct 2021
Coalitional Bayesian Autoencoders -- Towards explainable unsupervised deep learning Bang Xiang Yong Alexandra Brintrup 19 6 0 19 Oct 2021
Evaluating the Faithfulness of Importance Measures in NLP by Recursively Masking Allegedly Important Tokens and Retraining Andreas Madsen Nicholas Meade Vaibhav Adlakha Siva Reddy 96 35 0 15 Oct 2021
Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess Gregory Clark 25 9 0 05 Oct 2021
Longitudinal Distance: Towards Accountable Instance Attribution Rosina O. Weber Prateek Goel S. Amiri G. Simpson 11 0 0 23 Aug 2021
Temporal Dependencies in Feature Importance for Time Series Predictions Kin Kwan Leung Clayton Rooke Jonathan Smith S. Zuberi M. Volkovs OOD AI4TS 20 24 0 29 Jul 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning Yang Liu Sujay Khandagale Colin White W. Neiswanger 26 65 0 23 Jun 2021
FairCanary: Rapid Continuous Explainable Fairness Avijit Ghosh Aalok Shanbhag Christo Wilson 11 20 0 13 Jun 2021
On Sample Based Explanation Methods for NLP:Efficiency, Faithfulness, and Semantic Evaluation Wei Zhang Ziming Huang Yada Zhu Guangnan Ye Xiaodong Cui Fan Zhang 23 17 0 09 Jun 2021
The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations Peter Hase Harry Xie Mohit Bansal OODD LRM FAtt 18 91 0 01 Jun 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks Thorben Funke Megha Khosla Mandeep Rathee Avishek Anand FAtt 21 38 0 18 May 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 22 132 0 27 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 14 6 0 19 Apr 2021
Explaining COVID-19 and Thoracic Pathology Model Predictions by Identifying Informative Input Features Ashkan Khakzar Yang Zhang W. Mansour Yuezhi Cai Yawei Li Yucheng Zhang Seong Tae Kim Nassir Navab FAtt 36 16 0 01 Apr 2021
Deep learning on fundus images detects glaucoma beyond the optic disc Ruben Hemelings B. Elen J. Barbosa-Breda Matthew B. Blaschko P. Boever Ingeborg Stalmans MedIm 22 59 0 22 Mar 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 24 29 0 10 Mar 2021
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations N. Jethani Mukund Sudarshan Yindalon Aphinyanagphongs Rajesh Ranganath FAtt 82 70 0 02 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 18 57 0 25 Feb 2021
MIMIC-IF: Interpretability and Fairness Evaluation of Deep Learning Models on MIMIC-IV Dataset Chuizheng Meng Loc Trinh Nan Xu Yan Liu 18 30 0 12 Feb 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 32 169 0 13 Jan 2021
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 22 241 0 21 Nov 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization Judy Borowski Roland S. Zimmermann Judith Schepers Robert Geirhos Thomas S. A. Wallis Matthias Bethge Wieland Brendel FAtt 34 7 0 23 Oct 2020
Feature Importance Ranking for Deep Learning Maksymilian Wojtas Ke Chen 132 116 0 18 Oct 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Mingli Song Dacheng Tao Xinchao Wang FAtt 30 17 0 14 Oct 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 11 9 0 29 Sep 2020
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors Yi-Shan Lin Wen-Chuan Lee Z. Berkay Celik XAI 24 93 0 22 Sep 2020
Debiasing Concept-based Explanations with Causal Analysis M. T. Bahadori David Heckerman FAtt CML 6 38 0 22 Jul 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 17 73 0 24 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei-Yue Wang AAML 36 18 0 09 Jun 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 29 370 0 30 Apr 2020
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification Gang Liu Junchang Wang 20 60 0 08 Apr 2020