Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
This work addresses multimodal generative modeling for applications requiring joint and conditional generation, with incremental improvements in semi-supervised learning.
The authors tackled the problem of learning meaningful latent representations for multimodal data (e.g., images and text) to enable coherent joint generation and accurate conditional sampling, especially in semi-supervised settings where not all modalities are observed. They proposed a product-of-experts (PoE) based variational autoencoder, which empirically outperformed mixture-of-experts (MoE) and encoder-combined models in benchmarks.
Multimodal generative models should be able to learn a meaningful latent representation that enables a coherent joint generation of all modalities (e.g., images and text). Many applications also require the ability to accurately sample modalities conditioned on observations of a subset of the modalities. Often not all modalities may be observed for all training data points, so semi-supervised learning should be possible. In this study, we propose a novel product-of-experts (PoE) based variational autoencoder that have these desired properties. We benchmark it against a mixture-of-experts (MoE) approach and an approach of combining the modalities with an additional encoder network. An empirical evaluation shows that the PoE based models can outperform the contrasted models. Our experiments support the intuition that PoE models are more suited for a conjunctive combination of modalities.