Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints (2012.06718v1)

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.

• The paper presents a novel framework that enforces prediction constraints to maintain task-specific accuracy even with limited labeled data.
• It incorporates consistency constraints to ensure that predictions for reconstructed data align with those for the original data.
• The method leverages latent variables and spatial transformations to significantly improve semi-supervised learning performance in sparse data contexts.

The paper "Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints" (Hope et al., 2020 ) introduces a novel framework for enhancing the training of variational autoencoders and other deep generative models when working with sparse data. This approach uniquely integrates two principal constraints to improve model performance: prediction constraints and consistency constraints.

Key Concepts

1. Prediction Constraints: This method constrains the model so that predictions remain accurate, even when the generative model might otherwise lead to misspecified outcomes. By grounding the predictions in task-specific goals, the model remains robust and effective in semi-supervised scenarios where only a fraction of data points are labeled.
2. Consistency Constraints: These ensure that predictions for reconstructed data match those for the original data. These constraints are derived directly from the generative model and help the framework maintain high fidelity to the original data characteristics.

Benefits and Applications

The synergy between prediction and consistency constraints primarily bolsters performance in semi-supervised learning contexts. By guiding the model with these constraints, the framework can leverage unlabeled data effectively, which is often more abundant compared to labeled data. This trait is particularly advantageous for image classification tasks, where annotated datasets are scarce but unlabeled datasets are plentiful.

Additionally, this framework is enhanced by incorporating latent variables that capture spatial transformations, which further enriches the model's expressive power and its ability to generalize.

Comparison to Related Work

The paper's contributions can be placed in context with other significant works in the field:

• "Semi-Supervised Learning with Deep Generative Models" (Rezende et al., 2014 ): This earlier foundational work explores general semi-supervised learning with generative models. It emphasizes the utility of generative models in leveraging unlabeled data to generalize effectively from limited labeled datasets, whereas the current paper adds focused constraints to enhance prediction accuracy and consistency.
• "Learning Disentangled Representations with Semi-Supervised Deep Generative Models" (Siddharth et al., 2017 ): This paper aims at learning disentangled representations, which is different from the current paper's focus on prediction and consistency constraints but still shares a common goal of improving semi-supervised learning performance.
• "Deep Generative Models with Learnable Knowledge Constraints" (Hu et al., 2018 ): Here, constraints are used to incorporate domain knowledge flexibly into generative models, a concept that relates closely to the prediction and consistency constraints highlighted in the current paper.

In conclusion, the framework outlined in "Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints" offers a robust solution to enhancing deep generative models for semi-supervised learning, focusing specifically on maintaining accurate predictions and consistency with the original data. These innovations mark a valuable advancement in leveraging sparse data effectively.