Rank Selection of CP-decomposed Convolutional Layers with Variational Bayesian Matrix Factorization

TL;DR

This paper introduces a systematic method for selecting ranks in CP-decomposition of CNN layers using Variational Bayesian Matrix Factorization, improving compression efficiency and accuracy in neural network inference.

Contribution

It proposes a novel rank selection approach based on VBMF that adapts after each fine-tuning step, enhancing CNN compression performance.

Findings

Achieved better accuracy in compressed AlexNet layers.

Obtained higher compression rates compared to previous methods.

Demonstrated effectiveness of VBMF-based rank selection.

Abstract

Convolutional Neural Networks (CNNs) is one of successful method in many areas such as image classification tasks. However, the amount of memory and computational cost needed for CNNs inference obstructs them to run efficiently in mobile devices because of memory and computational ability limitation. One of the method to compress CNNs is compressing the layers iteratively, i.e. by layer-by-layer compression and fine-tuning, with CP-decomposition in convolutional layers. To compress with CP-decomposition, rank selection is important. In the previous approach rank selection that is based on sensitivity of each layer, the average rank of the network was still arbitrarily selected. Additionally, the rank of all layers were decided before whole process of iterative compression, while the rank of a layer can be changed after fine-tuning. Therefore, this paper proposes selecting rank of each layer using Variational Bayesian Matrix Factorization (VBMF) which is more systematic than arbitrary approach. Furthermore, to consider the change of each layer's rank after fine-tuning of previous iteration, the method is applied just before compressing the target layer, i.e. after fine-tuning of the previous iteration. The results show better accuracy while also having more compression rate in AlexNet's convolutional layers compression.