title: SGDR: Stochastic Gradient Descent with Warm Restarts
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{<a href="https://github.com/loshchil/SGDR">https://github.com/loshchil/SGDR</a>}
authors: Ilya Loshchilov Frank Hutter
hIndex: Frank Hutter 66