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We regard pre-trained residual networks (ResNets) as nonlinear systems and use linearization, a common method used in the qualitative analysis of nonlinear systems, to understand the behavior of the networks under small perturbations of the input images.
Applied Nonlinear Control
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A. Krizhevsky and G. Hinton · 2009
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Adversarial examples in the physical world
A. Kurakin, I. Goodfellow, and S. Bengio · 2016
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Resurrecting the Sigmoid in Deep Learning through Dynamical Isometry: Theory and Practice
J. Pennington, S. Schoenholz, and S. Ganguli · 2017
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The Emergence of Spectral Universality in Deep Networks
J. Pennington, S. S. Schoenholz, and S. Ganguli · 2018
Later among the works it cites.
Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function
W. Tarnowski, P. Warchoł, S. Jastrzębski, J. Tabor, and M. A. Nowak · 2018
Later among the works it cites.
L. Xiao, Y. Bahri, J. Sohl-Dickstein, S. S. Schoenholz, and J. Pennington · 2018
Later among the works it cites.
https://pytorch.org/ , 2019
Pytorch · 2019
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Large-Scale Image Classification Networks for Embedded Systems
O. Semery · 2019
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