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Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model.
The mnist database of handwritten digits
Yann LeCun · 1998
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Visualizing higher-layer features of a deep network
Dumitru Erhan, Yoshua Bengio, Aaron Courville, and Pascal Vincent · 2009
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How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert MÞller · 2010
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On random weights and unsupervised feature learning
Andrew M Saxe, Pang Wei Koh, Zhenghao Chen, Maneesh Bhand, Bipin Suresh, and Andrew Y Ng · 2011
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Making machine learning models interpretable
Alfredo Vellido, José David Martín-Guerrero, and Paulo JG Lisboa · 2012
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Interpretability issues in fuzzy modeling , volume 128
Jorge Casillas, Oscar Cordón, Francisco Herrera Triguero, and Luis Magdalena · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Striving for simplicity: The all convolutional net
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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Visualizing and understanding convolutional networks
Matthew D Zeiler and Rob Fergus · 2014
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Fulton Wang and Cynthia Rudin · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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European union regulations on algorithmic decision-making and a" right to explanation"
Bryce Goodman and Seth Flaxman · 2016
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Debugging machine learning models
Gabriel Cadamuro, Ran Gilad-Bachrach, and Xiaojin Zhu · 2016
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Not just a black box: Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, Anna Shcherbina, and Anshul Kundaje · 2016
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Why should i trust you?: Explaining the predictions of any classifier
Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Grad-cam: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
Cited alongside, same era.
Salient deconvolutional networks
Aravindh Mahendran and Andrea Vedaldi · 2016
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Cited alongside, same era.
Accountability of ai under the law: The role of explanation
Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O’Brien, Stuart Schieber, James Waldo, David Weinberger, and Alexandra Wood · 2017
The (un) reliability of saliency methods
Pieter-Jan Kindermans, Sara Hooker, Julius Adebayo, Maximilian Alber, Kristof T Schütt, Sven Dähne, Dumitru Erhan, and Been Kim · 2017
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Evaluating the visualization of what a deep neural network has learned
Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Lapuschkin, and Klaus-Robert Müller · 2017
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Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2017
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Understanding deep learning requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2017
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Dmitry Ulyanov, Andrea Vedaldi, and Victor Lempitsky · 2017
Later among the works it cites.
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Cited alongside, same era.
Interpretable & explorable approximations of black box models
Himabindu Lakkaraju, Ece Kamar, Rich Caruana, and Jure Leskovec · 2017
Cited alongside, same era.
Visualizing deep neural network decisions: Prediction difference analysis
Luisa M Zintgraf, Taco S Cohen, Tameem Adel, and Max Welling · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Ruth C Fong and Andrea Vedaldi · 2017
Cited alongside, same era.
Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
Later among the works it cites.
Learning how to explain neural networks: Patternnet and patternattribution
Maximilian Alber Klaus-Robert Müller Dumitru Erhan Been Kim Sven Dähne Pieter-Jan Kindermans, Kristof T. Schütt · 2018
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Learning to explain: An information-theoretic perspective on model interpretation
Jianbo Chen, Le Song, Martin Wainwright, and Michael Jordan · 2018
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A theoretical explanation for perplexing behaviors of backpropagation-based visualizations
Weili Nie, Yang Zhang, and Ankit Patel · 2018
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Automatic shadow detection in 2d ultrasound
Qingjie Meng, Christian Baumgartner, Matthew Sinclair, James Housden, Martin Rajchl, Alberto Gomez, Benjamin Hou, Nicolas Toussaint, Jeremy Tan, Jacqueline Matthew, et al · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2018
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Local explanation methods for deep neural networks lack sensitivity to parameter values
Julius Adebayo, Justin Gilmer, Ian Goodfellow, and Been Kim · 2018
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Noise-adding methods of saliency map as series of higher order partial derivative
Junghoon Seo, Jeongyeol Choe, Jamyoung Koo, Seunghyeon Jeon, Beomsu Kim, and Taegyun Jeon · 2018
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