Semi-Conditional Normalizing Flows for Semi-Supervised Learning

This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value an...

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Bibliographic Details
Published in:arXiv.org 2020-06
Main Authors: Atanov, Andrei, Volokhova, Alexandra, Ashukha, Arsenii, Sosnovik, Ivan, Vetrov, Dmitry
Format: Article
Language:English
Subjects:
Coders
Semi-supervised learning
Online Access:Get full text
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Summary:This paper proposes a semi-conditional normalizing flow model for semi-supervised learning. The model uses both labelled and unlabeled data to learn an explicit model of joint distribution over objects and labels. Semi-conditional architecture of the model allows us to efficiently compute a value and gradients of the marginal likelihood for unlabeled objects. The conditional part of the model is based on a proposed conditional coupling layer. We demonstrate performance of the model for semi-supervised classification problem on different datasets. The model outperforms the baseline approach based on variational auto-encoders on MNIST dataset.
ISSN:2331-8422