《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
1. 論文思想 訓練深度學習網絡是相當複雜的，每個層的輸入分佈會在訓練中隨着前一層的參數變化而改變。仔細地網絡初始化以及較低的學習率下會降低網絡的訓練速度，特別是具有飽和非線性的網絡。在該論文中將該中現象稱之爲「internal covariate shift」，在論文中爲了解決該問題提出了BN的概念，它取得的成果也會很顯著： （1）網絡中添加了BN層之後可以使得網絡使用更高的學習率，減少對網絡初
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相關文章
- 1. 論文筆記：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
- 3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯（轉）
- 4. 論文閱讀 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift論文翻譯
- 7. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 8. 【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 9. Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文筆記
- 10. Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift