Disentangling shared and private latent factors in multimodal Variational Autoencoders

arXiv:2403.06338v1 Announce Type: cross
Abstract: Generative models for multimodal data permit the identification of latent factors that may be associated with important determinants of observed data heterogeneity. Common or shared factors could be important for explaining variation across modalities whereas other factors may be private and important only for the explanation of a single modality. Multimodal Variational Autoencoders, such as MVAE and MMVAE, are a natural choice for inferring those underlying latent factors and separating shared variation from private. In …

abstract arxiv autoencoders cs.lg data generative generative models identification multimodal multimodal data q-bio.gn stat.ml type variation variational autoencoders