Residual Networks as Nonlinear Systems: Stability Analysis using Linearization
Kai Rothauge, Zhewei Yao, Zixi Hu, Michael W. Mahoney

TL;DR
This paper models pre-trained ResNets as nonlinear systems and uses linearization to analyze their stability and response to perturbations, revealing insights into the behavior of residual units and the impact of network modifications.
Contribution
It introduces a stability analysis framework for ResNets using linearization and singular value decomposition, providing new understanding of perturbation propagation and network robustness.
Findings
Most singular values of residual units are close to 1.
Scaling and weight adjustments significantly affect singular value distributions.
Adversarial perturbations exhibit a dramatic increase near the network's end.
Abstract
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. We work with ResNet-56 and ResNet-110 trained on the CIFAR-10 data set. We linearize these networks at the level of residual units and network stages, and the singular value decomposition is used in the stability analysis of these components. It is found that most of the singular values of the linearizations of residual units are 1 and, in spite of the fact that the linearizations depend directly on the activation maps, the singular values differ only slightly for different input images. However, adjusting the scaling of the skip connection or the values of the weights in a residual unit has a significant impact on the singular value…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Bacillus and Francisella bacterial research
