Understanding the Disharmony between Dropout and Batch Normalization by Variance Shift
This paper first answers the question "why do the two most powerful techniques Dropout and Batch Normalization (BN) often lead to a worse performance when they are combined together?" in both theoretical and statistical aspects. Theoretically, we find that Dropout would shift the variance of a specific neural unit when we transfer the state of that network from train to test. However, BN would mai
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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. We refer to this phenomenon as internal covar
Understanding the backward pass through Batch Normalization Layer
Understanding the backward pass through Batch Normalization Layer At the moment there is a wonderful course running at Standford University, called CS231n - Convolutional Neural Networks for Visual Recognition, held by Andrej Karpathy, Justin Johnson and Fei-Fei Li. Fortunately all the course material is provided for free and all the lectures are recorded and uploaded on Youtube. This class gives
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