Search Results for author: Yoshihiro Yamada

Found 4 papers, 1 papers with code

Joint Search of Data Augmentation Policies and Network Architectures

no code implementations17 Dec 2020 Taiga Kashima, Yoshihiro Yamada, Shunta Saito

In this paper, we propose a joint optimization method for data augmentation policies and network architectures to bring more automation to the design of training pipeline.

AutoML Data Augmentation

ShakeDrop Regularization for Deep Residual Learning

5 code implementations7 Feb 2018 Yoshihiro Yamada, Masakazu Iwamura, Takuya Akiba, Koichi Kise

In this paper, to relieve the overfitting effect of ResNet and its improvements (i. e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.

ShakeDrop regularization

no code implementations ICLR 2018 Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise

This paper proposes a powerful regularization method named \textit{ShakeDrop regularization}.

Deep Pyramidal Residual Networks with Separated Stochastic Depth

no code implementations5 Dec 2016 Yoshihiro Yamada, Masakazu Iwamura, Koichi Kise

On general object recognition, Deep Convolutional Neural Networks (DCNNs) achieve high accuracy.

Object Recognition

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