SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners

TL;DR

This paper introduces SCSP, a spectral clustering-based filter pruning method for CNNs that reduces model size efficiently while maintaining performance, using a self-adaptive approach for rapid pruning.

Contribution

The paper proposes a novel spectral clustering filter pruning technique with self-adaptive manners for CNN compression, enabling quick and effective model size reduction.

Findings

Achieves significant model compression with maintained accuracy.

Pruning process completes in fewer epochs due to self-adaptive strategy.

Provides a new perspective on interpreting model compression.

Abstract

Deep Convolutional Neural Networks (CNN) has achieved significant success in computer vision field. However, the high computational cost of the deep complex models prevents the deployment on edge devices with limited memory and computational resource. In this paper, we proposed a novel filter pruning for convolutional neural networks compression, namely spectral clustering filter pruning with soft self-adaption manners (SCSP). We first apply spectral clustering on filters layer by layer to explore their intrinsic connections and only count on efficient groups. By self-adaption manners, the pruning operations can be done in few epochs to let the network gradually choose meaningful groups. According to this strategy, we not only achieve model compression while keeping considerable performance, but also find a novel angle to interpret the model compression process.