UnNAS: Are Labels Necessary for Neural Architecture Search?
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
相关文章
- 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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相关文章
- 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