Multimodal Variational Autoencoders for Semi-Supervised Learning: In Defense of Product-of-Experts
Research output: Working paper › Preprint › Research
Final published version, 2.05 MB, PDF document
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.
|Number of pages||29|
|Publication status||Published - Jan 2022|
Number of downloads are based on statistics from Google Scholar and www.ku.dk
No data available