Title: Dynamical Isometry is Achieved in Residual Networks in a Universal Way for any Activation Function,
Abstract: We demonstrate that in residual neural networks (ResNets) dynamical isometry
is achievable irrespectively of the activation function used. We do that by
deriving, with the help of Free Probability and Random Matrix Theories, a
universal formula for the spectral density of the input-output Jacobian at
initialization, in the large network width and depth limit. The resulting
singular value spectrum depends on a single parameter, which we calculate for a
variety of popular activation functions, by analyzing the signal propagation in
the artificial neural network. We corroborate our results with numerical
simulations of both random matrices and ResNets applied to the CIFAR-10
classification problem. Moreover, we study the consequence of this universal
behavior for the initial and late phases of the learning processes. We conclude
by drawing attention to the simple fact, that initialization acts as a
confounding factor between the choice of activation function and the rate of
learning. We propose that in ResNets this can be resolved based on our results,
by ensuring the same level of dynamical isometry at initialization.