They used a variational autoencoder where the latent space representation is disentangled. That approach is a promising way to make it easier to navigate the l

They used a variational autoencoder where the latent space representation is disentangled. That approach is a promising way to make it easier to navigate the latent space as changes in one dimension will have a reduced or no influence on other aspects of the data encoded. Here is a nice overview on disentanglement with further references: https://paperswithcode.com/method/beta-vae https://paperswithcode.com/method/beta-vae