Automated Quantization and Retraining for Neural Network Models Without Labeled Data-Reference-Cited by-同舟云学术

Automated Quantization and Retraining for Neural Network Models Without Labeled Data

Published:2022 Issue: Volume:10 Page:73818-73834
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Thonglek Kundjanasith¹^ORCID,Takahashi Keichi¹^ORCID,Ichikawa Kohei¹^ORCID,Nakasan Chawanat²,Nakada Hidemoto³^ORCID,Takano Ryousei³,Leelaprute Pattara²^ORCID,Iida Hajimu¹^ORCID

Affiliation:

1. Department of Science and Technology, Division of Information Science, Nara Institute of Science and Technology, Nara, Japan

2. Department of Computer Engineering, Faculty of Engineering, Kasetsart University, Bangkok, Thailand

3. National Institute of Advanced Industrial Science and Technology, Tsukuba, Japan

Funder

Research Organization of Information and Systems (ROIS) National Institute of Informatics (NII) Open Collaborative Research

Japan Society for the Promotion of Science (JSPS) KAKENHI

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09828404.pdf?arnumber=9828404

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4. ReLeQ : A Reinforcement Learning Approach for Automatic Deep Quantization of Neural Networks

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