Sanity Checks for Saliency Maps

8 October 2018

Justin Gilmer

Papers citing "Sanity Checks for Saliency Maps"

50 / 302 papers shown

Title
Why Did This Model Forecast This Future? Closed-Form Temporal Saliency Towards Causal Explanations of Probabilistic Forecasts Chirag Raman Hayley Hung Marco Loog 16 3 0 01 Jun 2022
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
What You See is What You Classify: Black Box Attributions Steven Stalder Nathanael Perraudin R. Achanta F. Pérez-Cruz Michele Volpi FAtt 24 9 0 23 May 2022
B-cos Networks: Alignment is All We Need for Interpretability Moritz D Boehle Mario Fritz Bernt Schiele 31 84 0 20 May 2022
Cardinality-Minimal Explanations for Monotonic Neural Networks Ouns El Harzli Bernardo Cuenca Grau Ian Horrocks FAtt 30 5 0 19 May 2022
Trustworthy Graph Neural Networks: Aspects, Methods and Trends He Zhang Bang Wu Xingliang Yuan Shirui Pan Hanghang Tong Jian Pei 45 104 0 16 May 2022
How Does Frequency Bias Affect the Robustness of Neural Image Classifiers against Common Corruption and Adversarial Perturbations? Alvin Chan Yew-Soon Ong Clement Tan AAML 22 13 0 09 May 2022
ExSum: From Local Explanations to Model Understanding Yilun Zhou Marco Tulio Ribeiro J. Shah FAtt LRM 11 25 0 30 Apr 2022
Backdooring Explainable Machine Learning Maximilian Noppel Lukas Peter Christian Wressnegger AAML 14 5 0 20 Apr 2022
Interpretability of Machine Learning Methods Applied to Neuroimaging Elina Thibeau-Sutre S. Collin Ninon Burgos O. Colliot 16 4 0 14 Apr 2022
Visualizing Deep Neural Networks with Topographic Activation Maps A. Krug Raihan Kabir Ratul Christopher Olson Sebastian Stober FAtt AI4CE 25 3 0 07 Apr 2022
Predicting and Explaining Mobile UI Tappability with Vision Modeling and Saliency Analysis E. Schoop Xin Zhou Gang Li Zhourong Chen Björn Hartmann Yang Li HAI FAtt 29 32 0 05 Apr 2022
Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning Yuansheng Xie Soroush Vosoughi Saeed Hassanpour 19 2 0 30 Mar 2022
Visualizing Global Explanations of Point Cloud DNNs Hanxiao Tan 3DPC 37 7 0 17 Mar 2022
Evaluating Feature Attribution Methods in the Image Domain Arne Gevaert Axel-Jan Rousseau Thijs Becker D. Valkenborg T. D. Bie Yvan Saeys FAtt 16 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 20 41 0 15 Feb 2022
DermX: an end-to-end framework for explainable automated dermatological diagnosis Raluca Jalaboi F. Faye Mauricio Orbes-Arteaga D. Jørgensen Ole Winther A. Galimzianova MedIm 11 17 0 14 Feb 2022
Time to Focus: A Comprehensive Benchmark Using Time Series Attribution Methods Dominique Mercier Jwalin Bhatt Andreas Dengel Sheraz Ahmed AI4TS 14 11 0 08 Feb 2022
Towards a consistent interpretation of AIOps models Yingzhe Lyu Gopi Krishnan Rajbahadur Dayi Lin Boyuan Chen Zhen Ming Z. Jiang AI4CE 19 19 0 04 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 174 185 0 03 Feb 2022
Debiased-CAM to mitigate systematic error with faithful visual explanations of machine learning Wencan Zhang Mariella Dimiccoli Brian Y. Lim FAtt 17 1 0 30 Jan 2022
Post-Hoc Explanations Fail to Achieve their Purpose in Adversarial Contexts Sebastian Bordt Michèle Finck Eric Raidl U. V. Luxburg AILaw 26 77 0 25 Jan 2022
Deeply Explain CNN via Hierarchical Decomposition Mingg-Ming Cheng Peng-Tao Jiang Linghao Han Liang Wang Philip H. S. Torr FAtt 48 15 0 23 Jan 2022
Global explainability in aligned image modalities Justin Engelmann Amos Storkey Miguel O. Bernabeu FAtt 14 4 0 17 Dec 2021
HIVE: Evaluating the Human Interpretability of Visual Explanations Sunnie S. Y. Kim Nicole Meister V. V. Ramaswamy Ruth C. Fong Olga Russakovsky 66 114 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
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism J. M. M. Torres Sara E. Medina-DeVilliers T. Clarkson M. Lerner Giuseppe Riccardi 17 34 0 25 Nov 2021
An Analysis of the Influence of Transfer Learning When Measuring the Tortuosity of Blood Vessels Matheus V. da Silva Julie Ouellette Baptiste Lacoste C. H. Comin 11 7 0 19 Nov 2021
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications Adrien Bennetot Ivan Donadello Ayoub El Qadi M. Dragoni Thomas Frossard ... M. Trocan Raja Chatila Andreas Holzinger Artur Garcez Natalia Díaz Rodríguez XAI 24 7 0 13 Nov 2021
Self-Interpretable Model with TransformationEquivariant Interpretation Yipei Wang Xiaoqian Wang 24 23 0 09 Nov 2021
Defense Against Explanation Manipulation Ruixiang Tang Ninghao Liu Fan Yang Na Zou Xia Hu AAML 39 11 0 08 Nov 2021
Gradient Frequency Modulation for Visually Explaining Video Understanding Models Xinmiao Lin Wentao Bao Matthew Wright Yu Kong FAtt AAML 22 2 0 01 Nov 2021
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods Zohaib Salahuddin Henry C. Woodruff A. Chatterjee Philippe Lambin 13 301 0 01 Nov 2021
A Survey on the Robustness of Feature Importance and Counterfactual Explanations Saumitra Mishra Sanghamitra Dutta Jason Long Daniele Magazzeni AAML 9 58 0 30 Oct 2021
Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection Chunjong Park Anas Awadalla Tadayoshi Kohno Shwetak N. Patel OOD 19 29 0 26 Oct 2021
VAC-CNN: A Visual Analytics System for Comparative Studies of Deep Convolutional Neural Networks Xiwei Xuan Xiaoyu Zhang Oh-Hyun Kwon K. Ma HAI 16 17 0 25 Oct 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
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models S. Chatterjee Arnab Das Chirag Mandal Budhaditya Mukhopadhyay Manish Vipinraj Aniruddh Shukla R. Rao Chompunuch Sarasaen Oliver Speck A. Nürnberger MedIm 26 14 0 16 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
The Irrationality of Neural Rationale Models Yiming Zheng Serena Booth J. Shah Yilun Zhou 27 16 0 14 Oct 2021
TSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency Lin Cheng Pengfei Fang Yanjie Liang Liao Zhang Chunhua Shen Hanzi Wang FAtt 17 11 0 11 Oct 2021
Deep Synoptic Monte Carlo Planning in Reconnaissance Blind Chess Gregory Clark 25 9 0 05 Oct 2021
Consistent Explanations by Contrastive Learning Vipin Pillai Soroush Abbasi Koohpayegani Ashley Ouligian Dennis Fong Hamed Pirsiavash FAtt 18 21 0 01 Oct 2021
Focus! Rating XAI Methods and Finding Biases Anna Arias-Duart Ferran Parés Dario Garcia-Gasulla Víctor Giménez-Ábalos 13 32 0 28 Sep 2021
Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning? Gregory B. Rehm Chao Wang I. Cortés-Puch Chen-Nee Chuah Jason Y. Adams 20 1 0 25 Sep 2021
From Heatmaps to Structural Explanations of Image Classifiers Li Fuxin Zhongang Qi Saeed Khorram Vivswan Shitole Prasad Tadepalli Minsuk Kahng Alan Fern XAI FAtt 23 4 0 13 Sep 2021
IFBiD: Inference-Free Bias Detection Ignacio Serna Daniel DeAlcala Aythami Morales Julian Fierrez J. Ortega-Garcia CVBM 31 11 0 09 Sep 2021
Deriving Explanation of Deep Visual Saliency Models S. Malladi J. Mukhopadhyay M. Larabi S. Chaudhury FAtt XAI 16 0 0 08 Sep 2021
PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs V. Kamakshi Uday Gupta N. C. Krishnan 13 18 0 31 Aug 2021