Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer

Yerlan Idelbayev, Miguel A. Carreira-Perpinan; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 8049-8059

Abstract


Neural net compression can be achieved by approximating each layer's weight matrix by a low-rank matrix. The real difficulty in doing this is not in training the resulting neural net (made up of one low-rank matrix per layer), but in determining what the optimal rank of each layer is--effectively, an architecture search problem with one hyperparameter per layer. We show that, with a suitable formulation, this problem is amenable to a mixed discrete-continuous optimization jointly over the ranks and over the matrix elements, and give a corresponding algorithm. We show that this indeed can select ranks much better than existing approaches, making low-rank compression much more attractive than previously thought. For example, we can make a VGG network faster than a ResNet and with nearly the same classification error.

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[bibtex]
@InProceedings{Idelbayev_2020_CVPR,
author = {Idelbayev, Yerlan and Carreira-Perpinan, Miguel A.},
title = {Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}