Explaining Image Classifiers with Multiscale Directional Image Representation

22 November 2022

Stefan Kolek

Robert Windesheim

Héctor Andrade-Loarca

Gitta Kutyniok

Ron Levie

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Papers citing "Explaining Image Classifiers with Multiscale Directional Image Representation"

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Title
3VL: Using Trees to Improve Vision-Language Models' Interpretability Nir Yellinek Leonid Karlinsky Raja Giryes CoGe VLM 162 4 0 28 Dec 2023
Cartoon Explanations of Image Classifiers Stefan Kolek Duc Anh Nguyen Ron Levie Joan Bruna Gitta Kutyniok FAtt 72 16 0 07 Oct 2021
In-Distribution Interpretability for Challenging Modalities Cosmas Heiß Ron Levie Cinjon Resnick Gitta Kutyniok Joan Bruna 9 7 0 01 Jul 2020
PyTorch: An Imperative Style, High-Performance Deep Learning Library Adam Paszke Sam Gross Francisco Massa Adam Lerer James Bradbury ... Sasank Chilamkurthy Benoit Steiner Lu Fang Junjie Bai Soumith Chintala ODL 224 42,038 0 03 Dec 2019
Shearlets as Feature Extractor for Semantic Edge Detection: The Model-Based and Data-Driven Realm Héctor Andrade-Loarca Gitta Kutyniok Ozan Oktem 28 16 0 27 Nov 2019
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods Dylan Slack Sophie Hilgard Emily Jia Sameer Singh Himabindu Lakkaraju FAtt AAML MLAU 54 809 0 06 Nov 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks Ruth C. Fong Mandela Patrick Andrea Vedaldi AAML 50 413 0 18 Oct 2019
A Rate-Distortion Framework for Explaining Neural Network Decisions Jan Macdonald S. Wäldchen Sascha Hauch Gitta Kutyniok 33 40 0 27 May 2019
Searching for MobileNetV3 Andrew G. Howard Mark Sandler Grace Chu Liang-Chieh Chen Bo Chen ... Yukun Zhu Ruoming Pang Vijay Vasudevan Quoc V. Le Hartwig Adam 261 6,685 0 06 May 2019
Edge, Ridge, and Blob Detection with Symmetric Molecules Rafael Reisenhofer E. King 22 17 0 28 Jan 2019
Extraction of digital wavefront sets using applied harmonic analysis and deep neural networks Héctor Andrade-Loarca Gitta Kutyniok Ozan Oktem P. Petersen 54 15 0 05 Jan 2019
Explaining Image Classifiers by Counterfactual Generation C. Chang Elliot Creager Anna Goldenberg David Duvenaud VLM 37 265 0 20 Jul 2018
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 70 673 0 28 Jun 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 165 1,172 0 27 Jun 2018
RISE: Randomized Input Sampling for Explanation of Black-box Models Vitali Petsiuk Abir Das Kate Saenko FAtt 107 1,159 0 19 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 158 1,817 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 76 683 0 02 Nov 2017
Explaining Recurrent Neural Network Predictions in Sentiment Analysis L. Arras G. Montavon K. Müller Wojciech Samek FAtt 39 353 0 22 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 455 21,459 0 22 May 2017
Real Time Image Saliency for Black Box Classifiers P. Dabkowski Y. Gal 42 586 0 22 May 2017
Interpretable Explanations of Black Boxes by Meaningful Perturbation Ruth C. Fong Andrea Vedaldi FAtt AAML 48 1,514 0 11 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 108 5,920 0 04 Mar 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 205 19,796 0 07 Oct 2016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 519 16,765 0 16 Feb 2016
Deep Residual Learning for Image Recognition Kaiming He Xinming Zhang Shaoqing Ren Jian Sun MedIm 1.3K 192,638 0 10 Dec 2015
Evaluating the visualization of what a Deep Neural Network has learned Wojciech Samek Alexander Binder G. Montavon Sebastian Lapuschkin K. Müller XAI 102 1,189 0 21 Sep 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 738 149,474 0 22 Dec 2014
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 166 4,653 0 21 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 860 99,991 0 04 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 157 7,252 0 20 Dec 2013