[论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

时间 2021-01-12

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题目：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 作者：GOOGLE Mingxing Tan Quoc V. Le Motivation: 作者认为网络的扩展是对效果有很大帮助的，但是人们一直没有找到一个有效的扩展方法。通过人工去调整 depth, width, resolution 的放大或缩

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1. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
3. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
5. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. 论文阅读——EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
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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1. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
3. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
5. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. 论文阅读——EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
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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