Biosignal Generation and Latent Variable Analysis with Recurrent Generative Adversarial Networks

17 May 2019Shota HaradaHideaki HayashiSeiichi Uchida

The effectiveness of biosignal generation and data augmentation with biosignal generative models based on generative adversarial networks (GANs), which are a type of deep learning technique, was demonstrated in our previous paper. GAN-based generative models only learn the projection between a random distribution as input data and the distribution of training data.Therefore, the relationship between input and generated data is unclear, and the characteristics of the data generated from this model cannot be controlled... (read more)

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