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• W2947703335 abstract "In this paper, we propose a novel structure for a cross-modal data association, which is inspired by the recent research on the associative learning structure of the brain. We formulate the cross-modal association in Bayesian inference framework realized by a deep neural network with multiple variational auto-encoders and variational associators. The variational associators transfer the latent spaces between auto-encoders that represent different modalities. The proposed structure successfully associates even heterogeneous modal data and easily incorporates the additional modality to the entire network via the proposed cross-modal associator. Furthermore, the proposed structure can be trained with only a small amount of paired data since auto-encoders can be trained by unsupervised manner. Through experiments, the effectiveness of the proposed structure is validated on various datasets including visual and auditory data." @default.
• W2947703335 created "2019-06-07" @default.
• W2947703335 creator A5006852919 @default.
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• W2947703335 date "2019-05-30" @default.
• W2947703335 modified "2023-10-15" @default.
• W2947703335 title "Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators" @default.
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• W2947703335 hasPublicationYear "2019" @default.
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