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Status: Unchecked ×Keyword: internal covariate shift ×Clear all

2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

   Ioffe and Szegedy · arXiv (Cornell University) · 2015

   The paper introduces Batch Normalization, which normalizes layer inputs within each training mini-batch to speed up deep network training and improve image classification accuracy on ImageNet.

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   1. UncheckedThe authors say Batch Normalization lets neural networks be trained with much higher learning rates and with less care over how the starting parameters are set.“Batch Normalization allows us to use much higher learning rates and be less careful about initialization.”
   2. UncheckedBatch Normalization also works as a regularizer, and in some cases this removes the need to use Dropout when training a network.“It also acts as a regularizer, in some cases eliminating the need for Dropout.”