论文笔记：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

时间 2021-01-02

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论文：https://arxiv.org/abs/1905.11946 中心思想提出了一种同时对深度，宽度，尺度（这里指的是输入的分辨率）的网络扩展(scale)方式以往的工作仅限于在width /height/resolution(input resolution)三者之一进行扩展而同时调节这几个变量又费时费力本文发现3个变量只需要同时乘一个系数就能得到比较好的效果，称为compound

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1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
3. Efficientnet: Rethinking model scaling for convolutional neural networks（论文笔记）
4. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
5. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks-文献阅读笔记
9. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
10. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
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1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
3. Efficientnet: Rethinking model scaling for convolutional neural networks（论文笔记）
4. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
5. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks-文献阅读笔记
9. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
10. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

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