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.
