Automated Multi-Stage Compression of Neural Networks

被引：37

作者：

Gusak, Julia ^{[1
]}

Kholiavchenko, Maksym ^{[2
]}

Ponomarev, Evgeny ^{[1
]}

Markeeva, Larisa ^{[1
]}

Blagoveschensky, Philip ^{[1
,3
]}

Cichocki, Andrzej ^{[1
]}

Oseledets, Ivan ^{[1
]}

机构：

[1] Skolkovo Inst Sci & Technol, Moscow, Russia

[2] Innopolis Univ, Innopolis, Russia

[3] Huawei Noahs Ark Lab, Hong Kong, Peoples R China

来源：

2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW) | 2019年

基金：

俄罗斯科学基金会;

关键词：

D O I：

10.1109/ICCVW.2019.00306

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Low-rank tensor approximations are very promising for compression of deep neural networks. We propose a new simple and efficient iterative approach, which alternates low-rank factorization with 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.

引用

页码：2501 / 2508

页数：8

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