Batch Normalization：Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文笔记
0 摘要 当前神经网络层之前的神经网络层的参数变化，会引起神经网络每一层输入数据的分布产生变化，这使得训练一个深度神经网络变得复杂。通过设置较小的学习率以及更谨慎的初始化参数减慢了训练，并且由于非线性饱和（注：如sigmoid激活函数的非线性饱和问题），训练一个深度神经网络会非常困难。这种现象被称为internal covariate shift。解决这个问题需要对输入进行标准化。本文的方
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相关文章
- 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》论文笔记
- 3. 论文笔记：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 4. 【论文阅读笔记】——Batch Normalization:Accelerating Deep Network Traning by Reducing Internal Covariate Shift
- 5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文解读
- 6. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文翻译（转）
- 7. 论文阅读 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 8. [论文阅读] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift论文翻译
- 10. Batch normalization:accelerating deep network training by reducing internal covariate shift的笔记