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Negative Flux Aggregation to Estimate Feature Attributions

17 January 2023

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Papers citing "Negative Flux Aggregation to Estimate Feature Attributions"

22 / 22 papers shown

Title
Interpretability-Aware Vision Transformer Yao Qiang Chengyin Li Prashant Khanduri D. Zhu ViT 221 8 0 14 Sep 2023
Fast Axiomatic Attribution for Neural Networks Robin Hesse Simone Schaub-Meyer Stefan Roth 39 38 0 15 Nov 2021
Guided Integrated Gradients: An Adaptive Path Method for Removing Noise A. Kapishnikov Subhashini Venugopalan Besim Avci Benjamin D. Wedin Michael Terry Tolga Bolukbasi 97 95 0 17 Jun 2021
Explaining a Series of Models by Propagating Shapley Values Hugh Chen Scott M. Lundberg Su-In Lee TDI FAtt 68 129 0 30 Apr 2021
Do Input Gradients Highlight Discriminative Features? Harshay Shah Prateek Jain Praneeth Netrapalli AAML FAtt 72 59 0 25 Feb 2021
Transformer Interpretability Beyond Attention Visualization Hila Chefer Shir Gur Lior Wolf 137 664 0 17 Dec 2020
Improving Adversarial Robustness via Probabilistically Compact Loss with Logit Constraints X. Li Xiangrui Li Deng Pan D. Zhu AAML 43 17 0 14 Dec 2020
Investigating Saturation Effects in Integrated Gradients Vivek Miglani Narine Kokhlikyan B. Alsallakh Miguel Martin Orion Reblitz-Richardson FAtt 84 26 0 23 Oct 2020
XRAI: Better Attributions Through Regions A. Kapishnikov Tolga Bolukbasi Fernanda Viégas Michael Terry FAtt XAI 59 212 0 06 Jun 2019
Full-Gradient Representation for Neural Network Visualization Suraj Srinivas François Fleuret MILM FAtt 77 274 0 02 May 2019
A Benchmark for Interpretability Methods in Deep Neural Networks Sara Hooker D. Erhan Pieter-Jan Kindermans Been Kim FAtt UQCV 111 682 0 28 Jun 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
SmoothGrad: removing noise by adding noise D. Smilkov Nikhil Thorat Been Kim F. Viégas Martin Wattenberg FAtt ODL 204 2,226 0 12 Jun 2017
A Unified Approach to Interpreting Model Predictions Scott M. Lundberg Su-In Lee FAtt 1.1K 21,939 0 22 May 2017
Learning Important Features Through Propagating Activation Differences Avanti Shrikumar Peyton Greenside A. Kundaje FAtt 201 3,873 0 10 Apr 2017
Axiomatic Attribution for Deep Networks Mukund Sundararajan Ankur Taly Qiqi Yan OOD FAtt 188 5,989 0 04 Mar 2017
"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 194,020 0 10 Dec 2015
Explaining NonLinear Classification Decisions with Deep Taylor Decomposition G. Montavon Sebastian Lapuschkin Alexander Binder Wojciech Samek Klaus-Robert Muller FAtt 60 737 0 08 Dec 2015
Rethinking the Inception Architecture for Computer Vision Christian Szegedy Vincent Vanhoucke Sergey Ioffe Jonathon Shlens Z. Wojna 3DV BDL 883 27,373 0 02 Dec 2015
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,386 0 04 Sep 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