Internal Covariate Shift与Normalization
引言 Batch norm在深度学习中已经是一个常用且有效的方法，用来加速网络收敛及防止梯度消失等神经网络常见的问题，那么batch norm为什么有效呢？ 从源头出发，神经网络难以训练的原因是什么？ Internal Covariate Shift 深度神经网络涉及到很多层的叠加，而每一层的参数更新会导致上层的输入数据分布发生变化，通过层层叠加，高层的输入分布变化会非常剧烈，这就使得高层需要不断
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相关文章
- 1. Batch Normalization和Internal Covariate Shift
- 2. 深度学习——Internal Covariate Shift与Normalization
- 3. Covariate shift and batch normalization
- 4. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文解读
- 5. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文翻译（转）
- 6. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》论文笔记
- 7. Batch Normalization: Accelerating Deep Network Training byReducing Internal Covariate Shift论文学习
- 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