【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
基本情況 這是一篇cv.LG2015的文章，所屬領域爲computer vision. Abstract 在訓練由於前面層的參數的變化，會導致本層輸入分佈的變化，因此導致訓練一個DNN十分複雜，我們不得不降低訓練速度和小心的初始化.這種現象被成爲internal covariate shift. 解決上述問題，通常採用的方法是normalize輸入層.我們的方法力圖使規範化成爲模型架構的一部分，並
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- 1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
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相關文章
- 1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
- 2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯（轉）
- 3. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
- 4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 5. 論文閱讀 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 6. 論文筆記：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 7. [論文閱讀] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift論文翻譯
- 9. Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift
- 10. (11)[ICML15] Batch Norm: Accelerating Deep Network Training by Reducing Internal Covariate Shift