survey

Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap

Authors:

Jianlong Chang,

Xiaopeng Zhang, and

Qi TianAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 9

Article No.: 183, Pages 1 - 37

https://doi.org/10.1145/3473330

Published: 08 October 2021 Publication History

Abstract

Neural architecture search (NAS) has attracted increasing attention. In recent years, individual search methods have been replaced by weight-sharing search methods for higher search efficiency, but the latter methods often suffer lower instability. This article provides a literature review on these methods and owes this issue to the optimization gap. From this perspective, we summarize existing approaches into several categories according to their efforts in bridging the gap, and we analyze both advantages and disadvantages of these methodologies. Finally, we share our opinions on the future directions of NAS and AutoML. Due to the expertise of the authors, this article mainly focuses on the application of NAS to computer vision problems.

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Wu KZhan YAn YLi S(2024)Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image ClassificationRemote Sensing10.3390/rs1613232816:13(2328)Online publication date: 26-Jun-2024
https://doi.org/10.3390/rs16132328
Le DTran APham THuynh T(2024)A Survey of Model Compression and Its Feedback Mechanism in Federated LearningProceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval10.1145/3643488.3660293(37-42)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643488.3660293
Wang LTerrill CDivsalar DWesel R(2024)LDPC Decoding With Degree-Specific Neural Message Weights and RCQ DecodingIEEE Transactions on Communications10.1109/TCOMM.2023.334338772:4(1912-1924)Online publication date: Apr-2024
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Index Terms

Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gap
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 9

December 2022

800 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3485140

Editor:
Albert Zomaya
University of Sydney, Australia

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Copyright © 2021 Association for Computing Machinery.

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Publication History

Published: 08 October 2021

Accepted: 01 June 2021

Revised: 01 April 2021

Received: 01 September 2020

Published in CSUR Volume 54, Issue 9

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Cited By

Wu KZhan YAn YLi S(2024)Multiscale Feature Search-Based Graph Convolutional Network for Hyperspectral Image ClassificationRemote Sensing10.3390/rs1613232816:13(2328)Online publication date: 26-Jun-2024
https://doi.org/10.3390/rs16132328
Le DTran APham THuynh T(2024)A Survey of Model Compression and Its Feedback Mechanism in Federated LearningProceedings of the 5th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval10.1145/3643488.3660293(37-42)Online publication date: 10-Jun-2024
https://dl.acm.org/doi/10.1145/3643488.3660293
Wang LTerrill CDivsalar DWesel R(2024)LDPC Decoding With Degree-Specific Neural Message Weights and RCQ DecodingIEEE Transactions on Communications10.1109/TCOMM.2023.334338772:4(1912-1924)Online publication date: Apr-2024
https://doi.org/10.1109/TCOMM.2023.3343387
Zhang JZhang LLi DWang L(2024)A Unified Search Framework for Data Augmentation and Neural Architecture on Small-Scale Image Data SetsIEEE Transactions on Cognitive and Developmental Systems10.1109/TCDS.2023.327417716:2(501-510)Online publication date: Apr-2024
https://doi.org/10.1109/TCDS.2023.3274177
Shin ICho CKim S(2024)Method for Expanding Search Space With Hybrid Operations in DynamicNASIEEE Access10.1109/ACCESS.2024.335073212(10242-10253)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350732
Ma BZhang YXia Y(2024)Momentum recursive DARTSPattern Recognition10.1016/j.patcog.2024.110710156(110710)Online publication date: Dec-2024
https://doi.org/10.1016/j.patcog.2024.110710
Lyu BYang YCao YShi TChen YHuang TWen S(2024)A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architecturesNeural Networks10.1016/j.neunet.2024.106312175(106312)Online publication date: Jul-2024
https://doi.org/10.1016/j.neunet.2024.106312
Man YXie LQiao SZhou YShang D(2024)Differentiable architecture search with multi-dimensional attention for spiking neural networksNeurocomputing10.1016/j.neucom.2024.128181(128181)Online publication date: Jul-2024
https://doi.org/10.1016/j.neucom.2024.128181
Lyu BYang YCao YWang PZhu JChang JWen S(2024)Efficient multi-objective neural architecture search framework via policy gradient algorithmInformation Sciences: an International Journal10.1016/j.ins.2024.120186661:COnline publication date: 17-Apr-2024
https://dl.acm.org/doi/10.1016/j.ins.2024.120186
Boukraichi HAkkari NCasenave FRyckelynck D(2024)A priori compression of convolutional neural networks for wave simulatorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106973126:PCOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.106973
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