(DL Classification)Review: EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
Ref Tensorflow EfficientNet Pytorch EfficientNet and Pretrained 根據上圖所示，分類網絡模型往往是在width, height, resolution三個方向進行設計，所以作者想到，對三個因素都進行調整是否能得到一個更好的模型 Compound Model Scaling Width, Depth, Resolution分別對網絡模型的
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相關文章
- 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. EfficientNet 《EfficientNet:Rethinking model scaling for convolutional neural networks》
- 6. 論文閱讀：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. [論文解讀]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)論文筆記
- 9. 【論文解讀】EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 10. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 論文筆記