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[1Q3-GS-11-01] Category formation of real objects using Multimodal Variational Autoencoder
Keywords:Deep generative model, Multimodal topic model, Variational autoencoder
We propose a neural network-based unsupervised object categorization method for a robot using multimodal sensor information.
The method is an extension of Multimodal Variational Autoencoder (MVAE). In the proposed method, Dirichlet prior is introduced for giving MVAE a clustering capability in the same way as Multimodal latent Dirichlet allocation (MLDA) that has been used for multimodal object categorization by a robot.
We performed comparative experiments with MLDA using both real objects and synthetic data.
The results show that our proposed model has a reduced computational costs compared to MLDA without deteriorating the classification accuracy.
The method is an extension of Multimodal Variational Autoencoder (MVAE). In the proposed method, Dirichlet prior is introduced for giving MVAE a clustering capability in the same way as Multimodal latent Dirichlet allocation (MLDA) that has been used for multimodal object categorization by a robot.
We performed comparative experiments with MLDA using both real objects and synthetic data.
The results show that our proposed model has a reduced computational costs compared to MLDA without deteriorating the classification accuracy.
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