ShakeDrop Regularization
| Language | Japanese |
|---|---|
| Type | Proceedings of International Conferences |
| Authors | Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise |
| Title | ShakeDrop Regularization |
| Journal | Proc. 6th International Conference on Learning Representation (ICLR) Workshop |
| Pages | pp.1-4 |
| Number of pages | 4 |
| Location | Vancouver, Canada |
| Reviewed | Reviewed |
| Date | April 2018 |
| Research fields | Object Recognition, Deep Learning |
| URL | https://openreview.net/forum?id=Bymu6tJwz |
| Key | Yamada2018_ICLR (id=1613) |
Abstract
This paper proposes a powerful regularization method named 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.
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