Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations
Channel: Jan Stuehmer
Date: 2020-08-24

Jan Stuehmer, Richard E. Turner, Sebastian Nowizin. Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations. AISTATS 2020.


Recently there has been an increased interest in unsupervised learning of disentangled representations using the Variational Autoencoder (VAE) framework.  Most of the existing work  has  focused  largely  on  modifying  the variational cost function to achieve this goal. We first show that these modified cost functions, e.g. beta-VAE, simplify the tendency of variational inference to underfit, causing pathological over-pruning and over-orthogonalization of learned components.


As a complementary approach, we propose a novel prior for representation learning with VAEs that structures the latent space into a hierarchy of independent vector spaces. Our extensive  quantitative experiments demonstrate that the prior significantly mitigates the trade-off between reconstruction loss and disentanglement over the state of the art.


Paper: http://proceedings.mlr.press/v108/stuehmer20a/stuehmer20a.pdf
Code: https://github.com/microsoft/isa-vae/