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Poster
in
Workshop: Bayesian Deep Learning

Funnels: Exact maximum likelihood with dimensionality reduction

Samuel Klein · John Raine · Tobias Golling · Slava Voloshynovskiy · Sebastion Pina-Otey


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. This layer can also be used with convolutional and feed forward layers.

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