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Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping

22 February 2021

Papers citing "Believe The HiPe: Hierarchical Perturbation for Fast, Robust, and Model-Agnostic Saliency Mapping"

23 / 23 papers shown

Title
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction Melkamu Mersha Khang Lam Joseph Wood Ali AlShami Jugal Kalita XAI AI4TS 275 33 0 30 Aug 2024
Hoechst Is All You Need: Lymphocyte Classification with Deep Learning Jessica Cooper I. Um Ognjen Arandjelovic D. Harrison 92 4 0 09 Jul 2021
Evaluating Input Perturbation Methods for Interpreting CNNs and Saliency Map Comparison Lukas Brunke Prateek Agrawal Nikhil George AAML FAtt 50 13 0 26 Jan 2021
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method Satya M. Muddamsetty M. N. Jahromi Andreea-Emilia Ciontos Laura M. Fenoy T. Moeslund AAML 75 26 0 26 Jan 2021
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification Hanliang Jiang Fuhao Shen Fei Gao Weidong Han 52 85 0 19 Jan 2021
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies Weronika Hryniewska Przemysław Bombiński P. Szatkowski Paulina Tomaszewska A. Przelaskowski P. Biecek OOD 68 47 0 11 Dec 2020
Deceptive AI Explanations: Creation and Detection Johannes Schneider Christian Meske Michalis Vlachos 69 28 0 21 Jan 2020
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 73 416 0 18 Oct 2019
Interpretable machine learning: definitions, methods, and applications W. James Murdoch Chandan Singh Karl Kumbier R. Abbasi-Asl Bin Yu XAI HAI 195 1,444 0 14 Jan 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 141 1,967 0 08 Oct 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 181 1,171 0 19 Jun 2018
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations Weili Nie Yang Zhang Ankit B. Patel FAtt 127 151 0 18 May 2018
The (Un)reliability of saliency methods Pieter-Jan Kindermans Sara Hooker Julius Adebayo Maximilian Alber Kristof T. Schütt Sven Dähne D. Erhan Been Kim FAtt XAI 101 687 0 02 Nov 2017
Explanation in Artificial Intelligence: Insights from the Social Sciences Tim Miller XAI 247 4,272 0 22 Jun 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 67 591 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 76 1,525 0 11 Apr 2017
Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization Ramprasaath R. Selvaraju Michael Cogswell Abhishek Das Ramakrishna Vedantam Devi Parikh Dhruv Batra FAtt 321 20,023 0 07 Oct 2016
Top-down Neural Attention by Excitation Backprop Jianming Zhang Zhe Lin Jonathan Brandt Xiaohui Shen Stan Sclaroff 81 947 0 01 Aug 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 16,990 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 248 4,681 0 21 Dec 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 312 7,308 0 20 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,893 0 12 Nov 2013