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arXiv · 1905.12867

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

Dae Ung Jo·ByeongJu Lee·Jongwon Choi·Haanju Yoo·Jin Young Choi
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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.

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Dae Ung Jo, ByeongJu Lee, Jongwon Choi, Haanju Yoo, Jin Young Choi. 2019-05-30. Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators. https://arxiv.org/abs/1905.12867

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