[CVPR19]MnasNet: Platform-Aware Neural Architecture Search for Mobile
本篇發表於CVPR2019，作者爲Google的Quoc V Le。本篇使用的額scale方法應該是ICML2019發表EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks的姊妹篇。不僅如此，本篇提出的架構也是在移動端十分硬核的應用。 Abstract 爲移動設備設計卷積神經網絡(CNN)是一個挑戰，因爲移
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相關文章
- 1. Research Guide for Neural Architecture Search
- 2. Neural architecture search
- 3. Neural Architecture Search(NAS)
- 4. UnNAS: Are Labels Necessary for Neural Architecture Search?
- 5. 《Multinomial Distribution Learning for Effective Neural Architecture Search》
- 6. Neural Architecture Search — Limitations and Extensions
- 7. Efficient Neural Architecture Search via Parameter Sharing
- 8. NEURAL ARCHITECTURE SEARCH WITH REINFORCEMENT LEARNING
- 9. （轉）The Evolved Transformer - Enhancing Transformer with Neural Architecture Search
- 10. Fast and Practical Neural Architecture Search論文總結