Funnels: Exact maximum likelihood with dimensionality reduction

Code

• github.com/sambklein/funnels_repo
  OfficialIn paperpytorch★ 2

Abstract

Normalizing flows are diffeomorphic, typically dimension-preserving, models trained using the likelihood of the model. We use the SurVAE framework to construct dimension reducing surjective flows via a new layer, known as the funnel. We demonstrate its efficacy on a variety of datasets, and show it improves upon or matches the performance of existing flows while having a reduced latent space size. The funnel layer can be constructed from a wide range of transformations including restricted convolution and feed forward layers.

Tasks

Reproductions