Shake-Shake Regularization of 3-Branch Residual Networks
Abstract
The method introduced in this paper aims at helping computer vision practitioners faced with an overfit problem. The idea is to replace, in a 3-branch ResNet, the standard summation of residual branches by a stochastic affine combination. The largest tested model improves on the best single shot published result on CIFAR-10 by reaching 2.86% test error. Code is available at https://github.com/xgastaldi/shake-shake
Cite
Text
Gastaldi. "Shake-Shake Regularization of 3-Branch Residual Networks." International Conference on Learning Representations, 2017.Markdown
[Gastaldi. "Shake-Shake Regularization of 3-Branch Residual Networks." International Conference on Learning Representations, 2017.](https://mlanthology.org/iclr/2017/gastaldi2017iclr-shake/)BibTeX
@inproceedings{gastaldi2017iclr-shake,
title = {{Shake-Shake Regularization of 3-Branch Residual Networks}},
author = {Gastaldi, Xavier},
booktitle = {International Conference on Learning Representations},
year = {2017},
url = {https://mlanthology.org/iclr/2017/gastaldi2017iclr-shake/}
}