EfficientNet 《EfficientNet:Rethinking model scaling for convolutional neural networks》

时间 2021-01-12

标签深度学习文献繁體版

原文原文链接

作者想要达到算法准确度和效率的双重提升，一般提高准确度的方法主要是增加模型的深度或宽度，或者增加图像输入的分辨率。在准确率方面，自从2012年AlexNet赢得了ImageNet的冠军，卷积神经网络变得越来越流行并且越来越大，2014年冠军GoogleNet有6.8M的参数，2017年的SENet有145M的参数，最新的GPipe有557M的参数量，GPipe这么大的网络很难在普

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1. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks（2019）
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
3. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
5. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. 论文阅读：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（2019）
2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
3. [ICML19]EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
4. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
5. (DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
6. 论文阅读：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

>>更多相关文章<<