《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》论文笔记
1. 论文思想 训练深度学习网络是相当复杂的，每个层的输入分布会在训练中随着前一层的参数变化而改变。仔细地网络初始化以及较低的学习率下会降低网络的训练速度，特别是具有饱和非线性的网络。在该论文中将该中现象称之为“internal covariate shift”，在论文中为了解决该问题提出了BN的概念，它取得的成果也会很显著： （1）网络中添加了BN层之后可以使得网络使用更高的学习率，减少对网络初
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相关文章
- 1. 论文笔记：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文解读
- 3. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文翻译（转）
- 4. 论文阅读 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 5. [论文阅读] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift论文翻译
- 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
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