title: ShakeDrop regularization
abstract: This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.
  ShakeDrop is inspired by Shake-Shake regularization that decreases error rates by disturbing learning.
  While Shake-Shake can be applied to only ResNeXt which has multiple branches, ShakeDrop can be applied to not only ResNeXt but also ResNet, Wide ResNet and PyramidNet in a memory efficient way.
  Important and interesting feature of ShakeDrop is that it strongly disturbs learning by multiplying even a negative factor to the output of a convolutional layer in the forward training pass.
  The effectiveness of ShakeDrop is confirmed by experiments on CIFAR-10/100 and Tiny ImageNet datasets.
  
authors: Yoshihiro Yamada Masakazu Iwamura Koichi Kise
hIndex: Koichi Kise 25