Title: Improving Bi-directional Generation between Different Modalities with Variational Autoencoders,
Abstract: We investigate deep generative models that can exchange multiple modalities
bi-directionally, e.g., generating images from corresponding texts and vice
versa. A major approach to achieve this objective is to train a model that
integrates all the information of different modalities into a joint
representation and then to generate one modality from the corresponding other
modality via this joint representation. We simply applied this approach to
variational autoencoders (VAEs), which we call a joint multimodal variational
autoencoder (JMVAE). However, we found that when this model attempts to
generate a large dimensional modality missing at the input, the joint
representation collapses and this modality cannot be generated successfully.
Furthermore, we confirmed that this difficulty cannot be resolved even using a
known solution. Therefore, in this study, we propose two models to prevent this
difficulty: JMVAE-kl and JMVAE-h. Results of our experiments demonstrate that
these methods can prevent the difficulty above and that they generate
modalities bi-directionally with equal or higher likelihood than conventional
VAE methods, which generate in only one direction. Moreover, we confirm that
these methods can obtain the joint representation appropriately, so that they
can generate various variations of modality by moving over the joint
representation or changing the value of another modality.