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Motivated by distinct, though related, criteria, a growing number of attribution methods have been developed tointerprete deep learning.
How to explain individual classification decisions, 2009
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Mueller · 2009
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ImageNet: A Large-Scale Hierarchical Image Database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2013
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Matthew D Zeiler and Rob Fergus · 2013
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Very deep convolutional networks for large-scale image recognition, 2014
Karen Simonyan and Andrew Zisserman · 2014
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Striving for simplicity: The all convolutional net, 2014
Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin Riedmiller · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, Wojciech Samek, and Oscar Déniz Suárez · 2015
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Explaining nonlinear classification decisions with deep taylor decomposition, 2015
Grégoire Montavon, Sebastian Bach, Alexander Binder, Wojciech Samek, and Klaus-Robert Müller · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers, 2016
Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, and Wojciech Samek · 2016
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Algorithmic transparency via quantitative input influence: Theory and experiments with learning systems
A. Datta, S. Sen, and Y. Zick · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization, 2016
Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2016
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Learning Deep Features for Discriminative Localization
B. Zhou, A. Khosla, Lapedriza. A., A. Oliva, and A. Torralba · 2016
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Towards better understanding of gradient-based attribution methods for deep neural networks, 2017
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
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Smoothgrad: removing noise by adding noise, 2017
Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, and Martin Wattenberg · 2017
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Axiomatic attribution for deep networks
Mukund Sundararajan, Ankur Taly, and Qiqi Yan · 2017
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Sanity checks for saliency maps, 2018
Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
Aditya Chattopadhay, Anirban Sarkar, Prantik Howlader, and Vineeth N Balasubramanian · 2018
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Explaining explanations: An overview of interpretability of machine learning, 2018
Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael Specter, and Lalana Kagal · 2018
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Influence-directed explanations for deep convolutional networks, 2018
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A unified view of gradient-based attribution methods for deep neural networks
Marco Ancona, Enea Ceolini, Cengiz Öztireli, and Markus Gross · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers, 2017
Piotr Dabkowski and Yarin Gal · 2017
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K. Müller · 2017
Cited alongside, same era.
Learning important features through propagating activation differences, 2017
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje · 2017
Cited alongside, same era.
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, and Linyi Li · 2018
Later among the works it cites.
Influence-directed explanations for deep convolutional networks
Klas Leino, Shayak Sen, Anupam Datta, Matt Fredrikson, and Linyi Li · 2018
Later among the works it cites.
Methods for interpreting and understanding deep neural networks
Grégoire Montavon, Wojciech Samek, and Klaus-Robert Müller · 2018
Later among the works it cites.
Smooth grad-cam++: An enhanced inference level visualization technique for deep convolutional neural network models, 2019
Daniel Omeiza, Skyler Speakman, Celia Cintas, and Komminist Weldermariam · 2019
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