#Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

时间 2021-01-02

标签 paper reading DNN 繁體版

原文原文链接

论文题目: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文地址: https://arxiv.org/abs/1905.11946 论文发表于: ICML 2019 论文大体内容：本文主要提出了EfficientNet模型，来同时考虑网络的deep、width、resolution（分辨率）三

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1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
3. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
5. EfficientNet 《EfficientNet:Rethinking model scaling for convolutional neural networks》
6. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
9. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
10. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记
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1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
3. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. 论文阅读：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
5. EfficientNet 《EfficientNet:Rethinking model scaling for convolutional neural networks》
6. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
7. [论文解读]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)论文笔记
9. 【论文解读】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
10. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 论文笔记

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