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==Application==
Multimodal deep Boltzmann machines are successfully used in classification and missing data retrieval. The classification accuracy of multimodal deep Boltzmann machine outperforms [[support vector machine]]s, [[latent Dirichlet allocation]] and [[deep belief network]], when models are tested on data with both image-text modalities or with single modality. Multimodal deep Boltzmann machine is also able to predict the missing modality given the observed ones with reasonably good precision.
Self Supervised Learning brings more interesting and powerful model for multimodality. OpenAI developed CLIP and DALL-E models that revolutionized multimodality.
==See also==
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