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MUSCO: Multi-Stage Compression of neural networks

24 March 2019
Julia Gusak
Maksym Kholiavchenko
E. Ponomarev
L. Markeeva
Ivan Oseledets
A. Cichocki
ArXiv (abs)PDFHTML
Abstract

The low-rank tensor approximation is very promising for the compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with a smart rank selection and fine-tuning. We demonstrate the efficiency of our method comparing to non-iterative ones. Our approach improves the compression rate while maintaining the accuracy for a variety of tasks.

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