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3 claims from 1 paper

1. Computer Science › Stochastic Gradient Optimization Techniques

   Super-convergence: very fast training of neural networks using large learning rates

   Smith and Topin · 2019

   The paper describes "super-convergence", where neural networks train an order of magnitude faster than with standard methods, using one learning rate cycle with a large maximum learning rate.

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   1. UncheckedLarge learning rates act as a regulariser, so super-convergence training needs less of other regularisation to keep an optimal balance.“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.”
   2. UncheckedThe authors report that very fast 'super-convergence' training appeared across several image datasets and network architectures in their experiments.“Experiments demonstrate super-convergence for Cifar-10/100, MNIST and Imagenet datasets, and resnet, wide-resnet, densenet, and inception architectures.”
   3. UncheckedThe paper reports that super-convergence improves performance more, compared with standard training, when there is little labelled training data.“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.”