Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Understand

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.