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"

43 / 143 papers shown

Title
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 31 17 0 09 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
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methods E. Amparore Alan Perotti P. Bajardi FAtt 33 68 0 01 Jun 2021
Zorro: Valid, Sparse, and Stable Explanations in Graph Neural Networks Thorben Funke Megha Khosla Mandeep Rathee Avishek Anand FAtt 23 38 0 18 May 2021
Sanity Simulations for Saliency Methods Joon Sik Kim Gregory Plumb Ameet Talwalkar FAtt 41 17 0 13 May 2021
Do Feature Attribution Methods Correctly Attribute Features? Yilun Zhou Serena Booth Marco Tulio Ribeiro J. Shah FAtt XAI 33 132 0 27 Apr 2021
Improving Attribution Methods by Learning Submodular Functions Piyushi Manupriya Tarun Ram Menta S. Jagarlapudi V. Balasubramanian TDI 30 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 52 17 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 28 59 0 22 Mar 2021
Interpretable Machine Learning: Moving From Mythos to Diagnostics Valerie Chen Jeffrey Li Joon Sik Kim Gregory Plumb Ameet Talwalkar 32 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 88 69 0 02 Mar 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 28 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 24 30 0 12 Feb 2021
Convolutional Neural Network Interpretability with General Pattern Theory Erico Tjoa Cuntai Guan FAtt AI4CE 18 6 0 05 Feb 2021
Explainability of deep vision-based autonomous driving systems: Review and challenges Éloi Zablocki H. Ben-younes P. Pérez Matthieu Cord XAI 48 170 0 13 Jan 2021
Towards Robust Explanations for Deep Neural Networks Ann-Kathrin Dombrowski Christopher J. Anders K. Müller Pan Kessel FAtt 35 63 0 18 Dec 2020
Transformer Interpretability Beyond Attention Visualization Hila Chefer Shir Gur Lior Wolf 45 644 0 17 Dec 2020
Explaining by Removing: A Unified Framework for Model Explanation Ian Covert Scott M. Lundberg Su-In Lee FAtt 50 243 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 47 7 0 23 Oct 2020
Feature Importance Ranking for Deep Learning Maksymilian Wojtas Ke Chen 147 115 0 18 Oct 2020
Learning Propagation Rules for Attribution Map Generation Yiding Yang Jiayan Qiu Xiuming Zhang Dacheng Tao Xinchao Wang FAtt 38 17 0 14 Oct 2020
The elephant in the interpretability room: Why use attention as explanation when we have saliency methods? Jasmijn Bastings Katja Filippova XAI LRM 59 174 0 12 Oct 2020
Trustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach Nicholas F Halliwell Freddy Lecue FAtt 25 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 29 93 0 22 Sep 2020
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic Dataset Erico Tjoa Cuntai Guan XAI FAtt 19 27 0 07 Sep 2020
Debiasing Concept-based Explanations with Causal Analysis M. T. Bahadori David Heckerman FAtt CML 19 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 30 73 0 24 Jun 2020
Adversarial Infidelity Learning for Model Interpretation Jian Liang Bing Bai Yuren Cao Kun Bai Fei Wang AAML 57 18 0 09 Jun 2020
Explaining AI-based Decision Support Systems using Concept Localization Maps Adriano Lucieri Muhammad Naseer Bajwa Andreas Dengel Sheraz Ahmed 27 26 0 04 May 2020
Explainable Deep Learning: A Field Guide for the Uninitiated Gabrielle Ras Ning Xie Marcel van Gerven Derek Doran AAML XAI 43 371 0 30 Apr 2020
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification Gang Liu Junchang Wang 28 60 0 08 Apr 2020
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications Wojciech Samek G. Montavon Sebastian Lapuschkin Christopher J. Anders K. Müller XAI 51 82 0 17 Mar 2020
Ground Truth Evaluation of Neural Network Explanations with CLEVR-XAI L. Arras Ahmed Osman Wojciech Samek XAI AAML 21 150 0 16 Mar 2020
Measuring and improving the quality of visual explanations Agnieszka Grabska-Barwiñska XAI FAtt 24 3 0 14 Mar 2020
Selectivity considered harmful: evaluating the causal impact of class selectivity in DNNs Matthew L. Leavitt Ari S. Morcos 58 33 0 03 Mar 2020
Identifying Critical Neurons in ANN Architectures using Mixed Integer Programming M. Elaraby Guy Wolf Margarida Carvalho 26 5 0 17 Feb 2020
Explaining Explanations: Axiomatic Feature Interactions for Deep Networks Joseph D. Janizek Pascal Sturmfels Su-In Lee FAtt 30 143 0 10 Feb 2020
Making deep neural networks right for the right scientific reasons by interacting with their explanations P. Schramowski Wolfgang Stammer Stefano Teso Anna Brugger Xiaoting Shao Hans-Georg Luigs Anne-Katrin Mahlein Kristian Kersting 37 207 0 15 Jan 2020
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning Akanksha Atrey Kaleigh Clary David D. Jensen FAtt LRM 19 90 0 09 Dec 2019
Explainable AI for Trees: From Local Explanations to Global Understanding Scott M. Lundberg G. Erion Hugh Chen A. DeGrave J. Prutkin B. Nair R. Katz J. Himmelfarb N. Bansal Su-In Lee FAtt 28 286 0 11 May 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
Revisiting the Importance of Individual Units in CNNs via Ablation Bolei Zhou Yiyou Sun David Bau Antonio Torralba FAtt 59 115 0 07 Jun 2018