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In this paper, we propose a novel structure for a cross-modal dataassociation, which is inspired by the recent research on the associativelearning structure of the brain. We formulate the cross-modal association inBayesian inference framework realized by a deep neural network with multiplevariational auto-encoders and variational associators. The variationalassociators transfer the latent spaces between auto-encoders that representdifferent modalities. The proposed structure successfully associates evenheterogeneous modal data and easily incorporates the additional modality to theentire network via the proposed cross-modal associator. Furthermore, theproposed structure can be trained with only a small amount of paired data sinceauto-encoders can be trained by unsupervised manner. Through experiments, theeffectiveness of the proposed structure is validated on various datasetsincluding visual and auditory data.

Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators