Residual networks classify inputs based on their neural transient dynamics

TL;DR

This paper analyzes residual networks as dynamical systems, revealing how their transient residual dynamics contribute to classification, and introduces a method to optimize network depth during training for improved robustness and efficiency.

Contribution

It provides an analytical framework for understanding residual networks' classification based on transient dynamics and proposes a new method for adaptive depth adjustment during training.

Findings

Residual networks classify based on residual dynamics integration.

ResNets are more robust to input noise than MLPs.

Pruning residual network depth maintains high classification accuracy.

Abstract

We analyze the input-output behavior of residual networks from a dynamical system point of view by disentangling the residual dynamics from the output activities before the classification stage. For a network with simple skip connections between every successive layer, and for logistic activation function, and shared weights between layers, we show analytically that there is a cooperation and competition dynamics between residuals corresponding to each input dimension. Interpreting these kind of networks as nonlinear filters, the steady state value of the residuals in the case of attractor networks are indicative of the common features between different input dimensions that the network has observed during training, and has encoded in those components. In cases where residuals do not converge to an attractor state, their internal dynamics are separable for each input class, and the network can reliably approximate the output. We bring analytical and empirical evidence that residual networks classify inputs based on the integration of the transient dynamics of the residuals, and will show how the network responds to input perturbations. We compare the network dynamics for a ResNet and a Multi-Layer Perceptron and show that the internal dynamics, and the noise evolution are fundamentally different in these networks, and ResNets are more robust to noisy inputs. Based on these findings, we also develop a new method to adjust the depth for residual networks during training. As it turns out, after pruning the depth of a ResNet using this algorithm, the network is still capable of classifying inputs with a high accuracy.