Semi-Supervised Learning with Normalizing Flows

Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows...

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Bibliographic Details
Published in:arXiv.org 2019-12
Main Authors: Izmailov, Pavel, Kirichenko, Polina, Finzi, Marc, Andrew Gordon Wilson
Format: Article
Language:English
Subjects:
Image classification
Neural networks
Normalizing (statistics)
Optimization
Probabilistic models
Semi-supervised learning
Tables (data)
Online Access:Get full text
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Summary:Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an end-to-end approach to generative semi supervised learning with normalizing flows, using a latent Gaussian mixture model. FlowGMM is distinct in its simplicity, unified treatment of labelled and unlabelled data with an exact likelihood, interpretability, and broad applicability beyond image data. We show promising results on a wide range of applications, including AG-News and Yahoo Answers text data, tabular data, and semi-supervised image classification. We also show that FlowGMM can discover interpretable structure, provide real-time optimization-free feature visualizations, and specify well calibrated predictive distributions.
ISSN:2331-8422