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We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form.
Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
Earlier work this paper cites.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2015
Earlier work this paper cites.
Detecting adversarial samples from artifacts
R. Feinman, R. R. Curtin, S. Shintre, and A. B. Gardner · 2017
Earlier work this paper cites.
Learning efficient convolutional networks through network slimming
Z. Liu, J. Li, Z. Shen, G. Huang, S. Yan, and C. Zhang · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
K. Lee, K. Lee, H. Lee, and J. Shin · 2018
Later among the works it cites.
Characterizing adversarial subspaces using local intrinsic dimensionality
X. Ma, B. Li, Y. Wang, S. M. Erfani, S. Wijewickrema, G. Schoenebeck, D. Song, M. E. Houle, and J. Bailey · 2018
Later among the works it cites.
Methods for interpreting and understanding deep neural networks
G. Montavon, W. Samek, and K.-R. Müller · 2018
Later among the works it cites.
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