  Automated discovery of early visual concepts from raw image data is a major
open challenge in AI research. Addressing this problem, we propose an
unsupervised approach for learning disentangled representations of the
underlying factors of variation. We draw inspiration from neuroscience, and
show how this can be achieved in an unsupervised generative model by applying
the same learning pressures as have been suggested to act in the ventral visual
stream in the brain. By enforcing redundancy reduction, encouraging statistical
independence, and exposure to data with transform continuities analogous to
those to which human infants are exposed, we obtain a variational autoencoder
(VAE) framework capable of learning disentangled factors. Our approach makes
few assumptions and works well across a wide variety of datasets. Furthermore,
our solution has useful emergent properties, such as zero-shot inference and an
intuitive understanding of "objectness".
