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Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors

27 June 2020

Benjamin I. P. Rubinstein

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Papers citing "Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation Vectors"

23 / 23 papers shown

Title
Representational Similarity via Interpretable Visual Concepts Neehar Kondapaneni Oisin Mac Aodha Pietro Perona DRL 465 2 0 19 Mar 2025
Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models Itay Benou Tammy Riklin-Raviv 129 1 0 27 Feb 2025
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence Thao Le Tim Miller Ruihan Zhang L. Sonenberg Ronal Singh 69 0 0 13 May 2024
Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence Frederik Pahde Maximilian Dreyer Leander Weber Moritz Weckbecker Christopher J. Anders Thomas Wiegand Wojciech Samek Sebastian Lapuschkin 121 10 0 07 Feb 2022
Towards falsifiable interpretability research Matthew L. Leavitt Ari S. Morcos AAML AI4CE 80 68 0 22 Oct 2020
Explanations can be manipulated and geometry is to blame Ann-Kathrin Dombrowski Maximilian Alber Christopher J. Anders M. Ackermann K. Müller Pan Kessel AAML FAtt 81 334 0 19 Jun 2019
Metrics for Explainable AI: Challenges and Prospects R. Hoffman Shane T. Mueller Gary Klein Jordan Litman XAI 83 728 0 11 Dec 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 243 1,186 0 27 Jun 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) Been Kim Martin Wattenberg Justin Gilmer Carrie J. Cai James Wexler F. Viégas Rory Sayres FAtt 219 1,842 0 30 Nov 2017
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
Interpretable Convolutional Neural Networks Quanshi Zhang Ying Nian Wu Song-Chun Zhu FAtt 70 783 0 02 Oct 2017
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 204 2,235 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 22,002 0 22 May 2017
Network Dissection: Quantifying Interpretability of Deep Visual Representations David Bau Bolei Zhou A. Khosla A. Oliva Antonio Torralba MILM FAtt 148 1,515 1 19 Apr 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 203 3,879 0 10 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 20,070 0 07 Oct 2016
Generating Visual Explanations Lisa Anne Hendricks Zeynep Akata Marcus Rohrbach Jeff Donahue Bernt Schiele Trevor Darrell VLM FAtt 92 620 0 28 Mar 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,027 0 16 Feb 2016
Learning Deep Features for Discriminative Localization Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba SSL SSeg FAtt 250 9,326 0 14 Dec 2015
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 2.2K 194,322 0 10 Dec 2015
Convolutional Feature Masking for Joint Object and Stuff Segmentation Jifeng Dai Kaiming He Jian Sun SSeg 91 444 0 03 Dec 2014
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 1.7K 39,590 0 01 Sep 2014
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 595 15,893 0 12 Nov 2013