Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文筆記
0 摘要 當前神經網絡層之前的神經網絡層的參數變化，會引起神經網絡每一層輸入數據的分佈產生變化，這使得訓練一個深度神經網絡變得複雜。通過設置較小的學習率以及更謹慎的初始化參數減慢了訓練，並且由於非線性飽和（注：如sigmoid激活函數的非線性飽和問題），訓練一個深度神經網絡會非常困難。這種現象被稱爲internal covariate shift。解決這個問題需要對輸入進行標準化。本文的方
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相關文章
- 1. BN——Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 2. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
- 3. 論文筆記：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 4. 【論文閱讀筆記】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
- 5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
- 6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯（轉）
- 7. 論文閱讀 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 8. [論文閱讀] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift論文翻譯
- 10. Batch normalization:accelerating deep network training by reducing internal covariate shift的筆記