Fast Filter Pruning via Coarse-to-Fine Neural Architecture Search and Contrastive Knowledge Transfer-Reference-Cited by-同舟云学术

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Fast Filter Pruning via Coarse-to-Fine Neural Architecture Search and Contrastive Knowledge Transfer

Published:2023 Issue: Volume: Page:1-12
ISSN:2162-237X
Container-title:IEEE Transactions on Neural Networks and Learning Systems
language:
Short-container-title:IEEE Trans. Neural Netw. Learning Syst.

Author:

Lee Seunghyun¹,Song Byung Cheol¹^ORCID

Affiliation:

1. Department of Electronic Engineering, Inha University, Incheon, South Korea

Funder

Korean Government through Ministry of Science and Information and communications Technology

Artificial Intelligence Convergence Innovation Human Resources Development

AI Innovation Hub

Korean Government through MSIT

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Software

Link

http://xplorestaging.ieee.org/ielx7/5962385/6104215/10018843.pdf?arnumber=10018843

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. ARPruning: An automatic channel pruning based on attention map ranking;Neural Networks;2024-06

2. Boosting Convolutional Neural Networks With Middle Spectrum Grouped Convolution;IEEE Transactions on Neural Networks and Learning Systems;2024

3. Toward Less Constrained Macro-Neural Architecture Search;IEEE Transactions on Neural Networks and Learning Systems;2023

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