Disentangling Variational Autoencoders. (arXiv:2211.07700v1 [cs.LG])

A variational autoencoder (VAE) is a probabilistic machine learning framework
for posterior inference that projects an input set of high-dimensional data to
a lower-dimensional, latent space. The latent space learned with a VAE offers
exciting opportunities to develop new data-driven design processes in creative
disciplines, in particular, to automate the generation of multiple novel
designs that are aesthetically reminiscent of the input data but that were
unseen during training. However, the learned latent space is typically
disorganized and entangled: traversing …

arxiv variational autoencoders