SGDR: Stochastic Gradient Descent with Warm Restarts

Ilya Loshchilov, Frank Hutter

Nov 04, 2016 (modified: Mar 03, 2017) ICLR 2017 conference submission readers: everyone
  • Abstract: Restart techniques are common in gradient-free optimization to deal with multimodal functions. Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions. In this paper, we propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance when training deep neural networks. We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14\% and 16.21\%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the ImageNet dataset. Our source code is available at \\ \url{https://github.com/loshchil/SGDR}
  • TL;DR: We propose a simple warm restart technique for stochastic gradient descent to improve its anytime performance.
  • Conflicts: uni-freiburg.de
  • Keywords: Deep learning, Optimization

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