  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.
