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

Sergey IoffeChristian Szegedy

   Papers with code   Abstract  PDF

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities... (read more)

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