Internal Covariate Shift與Normalization
引言 Batch norm在深度學習中已經是一個常用且有效的方法，用來加速網絡收斂及防止梯度消失等神經網絡常見的問題，那麼batch norm爲什麼有效呢？ 從源頭出發，神經網絡難以訓練的原因是什麼？ Internal Covariate Shift 深度神經網絡涉及到很多層的疊加，而每一層的參數更新會導致上層的輸入數據分佈發生變化，通過層層疊加，高層的輸入分佈變化會非常劇烈，這就使得高層需要不斷
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相關文章
- 1. Batch Normalization和Internal Covariate Shift
- 2. 深度學習——Internal Covariate Shift與Normalization
- 3. Covariate shift and batch normalization
- 4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文解讀
- 5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 論文翻譯（轉）
- 6. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》論文筆記
- 7. Batch Normalization: Accelerating Deep Network Training byReducing Internal Covariate Shift論文學習
- 8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 9. Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift
- 10. 【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift