UnNAS: Are Labels Necessary for Neural Architecture Search?

时间 2021-01-02

标签 AutoML 深度学习人工智能繁體版

原文原文链接

无监督网络架构搜索当前业界主流的NAS算法都是利用有监督数据来搜索，然后再用有监督数据（可能和搜索数据不一样）来训练和评估。这篇论文提出一个崭新的方法，利用无监督的数据来搜索架构，称为Unsupervised Neural Architecture Search (UnNAS)。和其他的NAS算法一样，UnNAS也分成两个阶段：搜索阶段（Search Phase）和评估阶段（Evaluatio

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1. UnNAS:Are Labels Necessary for Neural Architecture Search?论文解析
2. Research Guide for Neural Architecture Search
3. Neural architecture search
4. Neural Architecture Search(NAS)
5. Neural Architecture Search — Limitations and Extensions
6. 《Multinomial Distribution Learning for Effective Neural Architecture Search》
7. [CVPR19]MnasNet: Platform-Aware Neural Architecture Search for Mobile
8. Video Architecture Search
9. （转）The Evolved Transformer - Enhancing Transformer with Neural Architecture Search
10. Efficient Neural Architecture Search via Parameter Sharing
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1. UnNAS:Are Labels Necessary for Neural Architecture Search?论文解析
2. Research Guide for Neural Architecture Search
3. Neural architecture search
4. Neural Architecture Search(NAS)
5. Neural Architecture Search — Limitations and Extensions
6. 《Multinomial Distribution Learning for Effective Neural Architecture Search》
7. [CVPR19]MnasNet: Platform-Aware Neural Architecture Search for Mobile
8. Video Architecture Search
9. （转）The Evolved Transformer - Enhancing Transformer with Neural Architecture Search
10. Efficient Neural Architecture Search via Parameter Sharing

>>更多相关文章<<