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arXiv · 2112.08069

Funnels: Exact maximum likelihood with dimensionality reduction

Samuel Klein·John A. Raine·Sebastian Pina-Otey·Slava Voloshynovskiy·Tobias Golling
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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.

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Samuel Klein, John A. Raine, Sebastian Pina-Otey, Slava Voloshynovskiy, Tobias Golling. 2021-12-15. Funnels: Exact maximum likelihood with dimensionality reduction. https://arxiv.org/abs/2112.08069

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