• Computer Science
  • Published 2018

ShakeDrop Regularization for Deep Residual Learning.

@inproceedings{Yamada2018ShakeDropRF,
  title={ShakeDrop Regularization for Deep Residual Learning.},
  author={Yoshihiro Yamada and Masakazu Iwamura and Takuya Akiba and Koichi Kise},
  year={2018}
}
Overfitting is a crucial problem in deep neural networks, even in the latest network architectures. In this paper, so as to relieve the overfitting effect of ResNet and its improvements (i.e., Wide ResNet, PyramidNet and ResNeXt), we propose a new regularization method, named ShakeDrop regularization. ShakeDrop is inspired by Shake-Shake, which is an effective regularization method but can be applied to only ResNeXt. ShakeDrop is even more effective than Shake-Shake and can be successfully… CONTINUE READING
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