  We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. Recently, some studies handle multiple modalities on deep generative
models, such as variational autoencoders (VAEs). However, these models
typically assume that modalities are forced to have a conditioned relation,
i.e., we can only generate modalities in one direction. To achieve our
objective, we should extract a joint representation that captures high-level
concepts among all modalities and through which we can exchange them
bi-directionally. As described herein, we propose a joint multimodal
variational autoencoder (JMVAE), in which all modalities are independently
conditioned on joint representation. In other words, it models a joint
distribution of modalities. Furthermore, to be able to generate missing
modalities from the remaining modalities properly, we develop an additional
method, JMVAE-kl, that is trained by reducing the divergence between JMVAE's
encoder and prepared networks of respective modalities. Our experiments show
that our proposed method can obtain appropriate joint representation from
multiple modalities and that it can generate and reconstruct them more properly
than conventional VAEs. We further demonstrate that JMVAE can generate multiple
modalities bi-directionally.
