UnNAS:Are Labels Necessary for Neural Architecture Search?论文解析
Are Labels Necessary for Neural Architecture Search? 何凯明和Ross Girshick再次联手，在AutoML领域发力，文章在3月27日刚刚能够搜索到。 这篇文章的主旨思想是：研究NAS的新方向，即利用无人工Label的数据集实现NAS的效果，并且通过实验证明，非监督的方法与监督方法相相比较在多数情况下相关性较大(>0.8)，而在一些任务中，无
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相关文章
- 1. UnNAS: Are Labels Necessary for Neural Architecture Search?
- 2. 论文解析：NASE: Learning Knowledge Graph Embedding for Link Prediction via Neural Architecture Search
- 3. Research Guide for Neural Architecture Search
- 4. NAS论文笔记——Progressive Neural Architecture Search
- 5. 论文笔记——NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 6. 论文笔记：Progressive Neural Architecture Search
- 7. 论文笔记《Neural Architecture Search With Reinforcement Learning》
- 8. Progressive Neural Architecture Search（论文阅读）
- 9. Neural Architecture Search with Reinforcement Learning论文总结
- 10. Fast and Practical Neural Architecture Search论文总结