1 research outputs found
Cross-modal Variational Auto-encoder with Distributed Latent Spaces and Associators
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.Comment: 10 pages, 6 figure