笔记——Learning Transferable Architectures for Scalable Image Recognition
主要思想： 通过NAS(Neural Architecture Search Framework)在CIFAR-10 dataset上找到最优的网络结构，再扩展到ImageNet dataset上。 模型提升—— This paper 1.2%better computer cost: a reductionof 28% 定义优化问题： 定义两种类型的convolution layer：
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相关文章
- 1. Learning Transferable Architectures for Scalable image recognition
- 2. Learning Transferable Architectures for Scalable Image Recognition
- 3. Paper Reading:Learning Transferable Architectures for Scalable Image Recognition
- 4. 自学网络结构（二）：Learning Transferable Architectures for Scalable Image Recognition
- 5. Deep Residual Learning for Image Recognition 笔记
- 6. 【论文笔记】Deep Residual Learning for Image Recognition
- 7. NASNet:Learning Transferable Architectures for Scalable Image Recognition 学习可伸缩的图像识别的可转移架构
- 8. Deep Residual Learning for Image Recognition阅读笔记
- 9. Deep Residual Learning for Image Recognition笔记
- 10. Deep Residual Learning for Image Recognition 论文笔记