Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks

24 Feb 2020Soham DeSamuel L. Smith

Batch normalization dramatically increases the largest trainable depth of residual networks, and this benefit has been crucial to the empirical success of deep residual networks on a wide range of benchmarks. We show that this key benefit arises because, at initialization, batch normalization downscales the residual branch relative to the skip connection, by a normalizing factor on the order of the square root of the network depth... (read more)

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