【inv2】Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
基本情况 这是一篇cv.LG2015的文章，所属领域为computer vision. Abstract 在训练由于前面层的参数的变化，会导致本层输入分布的变化，因此导致训练一个DNN十分复杂，我们不得不降低训练速度和小心的初始化.这种现象被成为internal covariate shift. 解决上述问题，通常采用的方法是normalize输入层.我们的方法力图使规范化成为模型架构的一部分，并
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相关文章
- 1. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文解读
- 2. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift 论文翻译（转）
- 3. 《Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift》论文笔记
- 4. 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. 论文笔记：Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- 7. [论文阅读] Batch Normalization: Accelerating Deep Network Training By Reducing Internal Covariate Shift
- 8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift论文翻译
- 9. Batch Normalization: Accelerating Deep Network Training b y Reducing Internal Covariate Shift
- 10. (11)[ICML15] Batch Norm: Accelerating Deep Network Training by Reducing Internal Covariate Shift