  Layer-sequential unit-variance (LSUV) initialization - a simple method for
weight initialization for deep net learning - is proposed. The method consists
of the two steps. First, pre-initialize weights of each convolution or
inner-product layer with orthonormal matrices. Second, proceed from the first
to the final layer, normalizing the variance of the output of each layer to be
equal to one.
  Experiment with different activation functions (maxout, ReLU-family, tanh)
show that the proposed initialization leads to learning of very deep nets that
(i) produces networks with test accuracy better or equal to standard methods
and (ii) is at least as fast as the complex schemes proposed specifically for
very deep nets such as FitNets (Romero et al. (2015)) and Highway (Srivastava
et al. (2015)).
  Performance is evaluated on GoogLeNet, CaffeNet, FitNets and Residual nets
and the state-of-the-art, or very close to it, is achieved on the MNIST,
CIFAR-10/100 and ImageNet datasets.
