Shake-Shake regularization
Machine Learning
2017-05-24 v2 Computer Vision and Pattern Recognition
Abstract
The method introduced in this paper aims at helping deep learning practitioners faced with an overfit problem. The idea is to replace, in a multi-branch network, the standard summation of parallel branches with a stochastic affine combination. Applied to 3-branch residual networks, shake-shake regularization improves on the best single shot published results on CIFAR-10 and CIFAR-100 by reaching test errors of 2.86% and 15.85%. Experiments on architectures without skip connections or Batch Normalization show encouraging results and open the door to a large set of applications. Code is available at https://github.com/xgastaldi/shake-shake
Cite
@article{arxiv.1705.07485,
title = {Shake-Shake regularization},
author = {Xavier Gastaldi},
journal= {arXiv preprint arXiv:1705.07485},
year = {2017}
}