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文献の詳細

論文の言語 日本語
著者 Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise
論文名 ShakeDrop Regularization
論文誌名 6th International Conference on Learning Representation (ICLR) Workshop
ページ pp.1-4
ページ数 4 pages
査読の有無
年月 2018年4月
要約 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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