Title: Normalization of Neural Networks using Analytic Variance Propagation,
Abstract: We address the problem of estimating statistics of hidden units in a neural
network using a method of analytic moment propagation. These statistics are
useful for approximate whitening of the inputs in front of saturating
non-linearities such as a sigmoid function. This is important for
initialization of training and for reducing the accumulated scale and bias
dependencies (compensating covariate shift), which presumably eases the
learning. In batch normalization, which is currently a very widely applied
technique, sample estimates of statistics of hidden units over a batch are
used. The proposed estimation uses an analytic propagation of mean and variance
of the training set through the network. The result depends on the network
structure and its current weights but not on the specific batch input. The
estimates are suitable for initialization and normalization, efficient to
compute and independent of the batch size. The experimental verification well
supports these claims. However, the method does not share the generalization
properties of BN, to which our experiments give some additional insight.