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The problem of attributing a deep network's prediction to its \emph{input/base} features is well-studied.
How to explain individual classification decisions
David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, and Klaus-Robert Müller · 2010
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Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller · 2014
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Yoon Kim · 2014
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Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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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, and Wojciech Samek · 2015
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Understanding intermediate layers using linear classifier probes
Guillaume Alain and Yoshua Bengio · 2016
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A unified approach to interpreting model predictions
Scott M Lundberg and Su-In Lee · 2017
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Feature Visualization
Alexander Mordvintse, Christopher Olah, and Ludwig Schubert · 2017
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Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017
Doina Precup and Yee Whye Teh, editors · 2017
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Influence-directed explanations for deep convolutional networks, 2018
Anupam Datta, Matt Fredrikson, Klas Leino, Linyi Li, and Shayak Sen · 2018
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TCAV: Relative concept importance testing with linear concept activation vectors, 2018
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Wojciech Samek, Alexander Binder, Grégoire Montavon, Sebastian Bach, and Klaus-Robert Müller · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Alexander Binder, Grégoire Montavon, Sebastian Bach, Klaus-Robert Müller, and Wojciech Samek · 2016
Cited alongside, same era.
Learning important features through propagating activation differences
Avanti Shrikumar, Peyton Greenside, and Anshul Kundaje
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Axiomatic attribution for deep networks
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On the importance of single directions for generalization
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