Title: Super-Convergence: Very Fast Training of Residual Networks Using Large Learning Rates. Abstract: 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.
