  In this paper, we describe a phenomenon, which we named "super-convergence",
where neural networks can be trained an order of magnitude faster than 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 one learning rate cycle and a large maximum
learning rate. A primary insight that allows super-convergence training is that
large learning rates regularize the training, hence requiring a reduction of
all other forms of regularization in order to preserve an optimal
regularization balance. We also derive a simplification of the Hessian Free
optimization method to compute an estimate of the optimal learning rate.
Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet
datasets, and resnet, wide-resnet, densenet, and inception architectures. 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. The architectures and code to replicate the figures in this
paper are available at github.com/lnsmith54/super-convergence. See
http://www.fast.ai/2018/04/30/dawnbench-fastai/ for an application of
super-convergence to win the DAWNBench challenge (see
https://dawn.cs.stanford.edu/benchmark/).
