Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints

TL;DR

This paper introduces a novel deep generative modeling framework that incorporates prediction and consistency constraints to improve semi-supervised image classification, especially with sparse labels.

Contribution

It proposes a new method combining variational autoencoders with prediction and consistency constraints to enhance semi-supervised learning.

Findings

Improved semi-supervised classification accuracy.

Effective use of unlabeled data to boost performance.

Enhanced generative models with spatial transformation latent variables.

Abstract

We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals. Our framework optimizes model parameters to maximize a variational lower bound on the likelihood of observed data, subject to a task-specific prediction constraint that prevents model misspecification from leading to inaccurate predictions. We further enforce a consistency constraint, derived naturally from the generative model, that requires predictions on reconstructed data to match those on the original data. We show that these two contributions -- prediction constraints and consistency constraints -- lead to promising image classification performance, especially in the semi-supervised scenario where category labels are sparse but unlabeled data is plentiful. Our approach enables advances in generative modeling to directly boost semi-supervised classification performance, an ability we demonstrate by augmenting deep generative models with latent variables capturing spatial transformations.