[論文解讀]ICML 2019|EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
題目：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks 作者：GOOGLE Mingxing Tan Quoc V. Le Motivation: 作者認爲網絡的擴展是對效果有很大幫助的，但是人們一直沒有找到一個有效的擴展方法。通過人工去調整 depth, width, resolution 的放大或縮
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相關文章
- 1. #Paper Reading# EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 2. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 3. 【論文解讀】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. 論文閱讀：EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 7. 論文閱讀——EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
- 8. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks (2019)論文筆記
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- 10. 論文瀏覽(6) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks