AI RESEARCH
Disentanglement of Sources in a Multi-Stream Variational Autoencoder
arXiv CS.LG
•
ArXi:2510.15669v2 Announce Type: replace-cross Variational autoencoders (VAEs) are among leading approaches to address the problem of learning disentangled representations. Typically a single VAE is used and disentangled representations are sought within its single continuous latent space. In this paper, we propose and provide a proof of concept for a novel Multi-Stream Variational Autoencoder (MS-VAE) that achieves disentanglement of sources by combining discrete and continuous latents.