[ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
个人觉得idea很不错，中心思想是直接对整体架构进行scale以达到减少参数的目的，也是Quoc V. Le刚发表在ICML19的文章，并且在Imagenet达到了最新的state-of-art级别的work。pytorch版本 Abstract 卷积神经网络(ConvNets)通常是在固定的资源预算下开发的，如果有更多的资源可用，则会进行扩展以获得更高的精度。在本文中，我们系统地研究了模型缩放，
相关文章
- 1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
- 2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 3. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 4. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 5. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 6. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
- 8. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 9. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
- 10. 论文浏览(6) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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相关文章
- 1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
- 2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 3. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 4. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 5. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 6. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
- 8. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 9. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
- 10. 论文浏览(6) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks