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MUSCO: Multi-Stage Compression of neural networks
- Publication Year :
- 2019
-
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.
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.1903.09973
- Document Type :
- Working Paper