Layer Collapse Can be Induced by Unstructured Pruning

TL;DR

This paper demonstrates that unstructured pruning can induce layer collapse in neural networks by reducing neuron entropy, enabling the removal of entire layers without significant performance loss.

Contribution

It introduces neuron entropy as a measure and shows how unstructured pruning can lead to layer removal, a structural effect previously thought unlikely.

Findings

Unstructured pruning lowers neuron entropy, causing some layers to become linearizable.

Layer removal through pruning does not significantly degrade performance.

The phenomenon is validated across CNNs, Vision Transformers, and NLP models.

Abstract

Unstructured pruning is a popular compression method for efficiently reducing model parameters. However, while it effectively decreases the number of parameters, it is commonly believed that unstructured pruning cannot shorten the computational critical path, i.e., the maximum number of layers traversed during forward propagation. In this paper, we study when and how unstructured pruning can yield structural effects. For rectifier-activated networks, we introduce the notion of neuron entropy, which quantifies the degree of nonlinearity utilization. We show that magnitude-based pruning naturally lowers this entropy, sometimes down to zero-entropy layers that become linearizable and can thus be removed. Building on this insight, we propose a method that leverages "unstructured" pruning to favor sparsity in low-entropy layers, enabling their complete removal. We validate the phenomenon across CNNs, Vision Transformers, and NLP models: unstructured pruning can induce effective layer removal with little or no performance degradation in over-parameterized networks.