In this paper, we show a phenomenon, which we named ``super-convergence'', where residual networks can be trained using an order of magnitude fewer iterations than is used with standard training methods.    The existence of super-convergence is relevant to understanding why deep networks generalize well.   One of the key elements of super-convergence is training with cyclical learning rates and a large maximum learning rate.   Furthermore, we present evidence that training with large learning rates improves performance by regularizing the network. In addition, we show that super-convergence provides a  greater boost in performance relative to standard training when the amount of labeled training data is limited.   We also derive a simplification of the Hessian Free optimization method to compute an estimate of the optimal learning rate.   The architectures to replicate this work will be made available upon publication.

Empirical proof of a new phenomenon requires new theoretical insights and is relevent to the active discussions in the literature on SGD and understanding generalization. The paper discusses a phenomenon where neural network training in very specific settings can profit much from a schedule including large learning rates The authors analyze training of residual networks using large cyclic learning rates, and demonstrate fast convergence with cyclic learning rates and evidence of large learning rates acting as regularization.