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The ability of to explain neural network decisions goes hand in hand with their safe deployment.
How to explain individual classification decisions
Baehrens, D., Schroeter, T., Harmeling, S., Kawanabe, M., Hansen, K., and Müller, K.-R · 2010
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M. A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., and Torralba, A · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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Distilling the knowledge in a neural network
Hinton, G. E., Vinyals, O., and Dean, J · 2015
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., and Torralba, A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K.-R · 2016
Cited alongside, same era.
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
Cited alongside, same era.
A unified view of gradient-based attribution methods for deep neural networks
Ancona, M. B., Ceolini, E., Öztireli, A. C., and Gross, M. H · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F. B., and Wattenberg, M · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I. J., Hardt, M., and Kim, B · 2018
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Github, 2018
lucid · 2018
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A benchmark for interpretability methods in deep neural networks
Hooker, S., Erhan, D., Kindermans, P.-J., and Kim, B · 2019
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
Cited alongside, same era.
European union regulations on algorithmic decision-making and a “right to explanation”
Goodman, B. and Flaxman, S · 2017
Cited alongside, same era.
The (un)reliability of saliency methods
Kindermans, P.-J., Hooker, S., Adebayo, J., Alber, M., Schütt, K. T., Dähne, S., Erhan, D., and Kim, B · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
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Aggregating explainability methods for neural networks stabilizes explanations
Rieger, L. and Hansen, L. K · 2019
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Full-gradient representation for neural network visualization
Srinivas, S. and Fleuret, F · 2019
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Benchmarking attribution methods with relative feature importance
Yang, M. and Kim, B · 2019
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