Auxiliary Deep Generative Models (1602.05473v4)

Abstract: Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST, SVHN and NORB datasets.

• The paper integrates auxiliary variables into deep generative models to better approximate complex latent distributions and improve convergence.
• It presents the Skip Deep Generative Model (SDGM) with dual stochastic layers and skip connections for enhanced model expressiveness.
• The approach achieves state-of-the-art semi-supervised classification (0.96% error on MNIST) and competitive log-likelihood results.

Overview of Auxiliary Deep Generative Models

The paper "Auxiliary Deep Generative Models" introduces an approach to enhance the capacity of deep generative models using auxiliary variables. This methodology aims to refine variational inference techniques, which have been instrumental in developing effective deep generative models for unsupervised and semi-supervised learning tasks. The paper provides rigorous evaluations, demonstrating improved performance on datasets such as MNIST, SVHN, and NORB.

Technical Contributions

The principal innovation of this work is the integration of auxiliary variables into the generative modeling framework. By introducing these variables, the authors aim to achieve a more expressive variational distribution without altering the underlying generative model structure. Notably, the auxiliary variables contribute to a better approximation of complex latent distributions, facilitating models that converge more rapidly and effectively.

The research also proposes a Skip Deep Generative Model (SDGM) with two stochastic layers and skip connections. This model builds upon the auxiliary framework, further enhancing expressiveness and training stability.

Key Findings

The paper reports substantial improvements on several fronts:

1. Expressive Variational Distributions: By leveraging auxiliary variables, the authors enable the modeling of complex non-Gaussian distributions, which are typically challenging with traditional mean field approximations.
2. Enhanced Classification Performance: The proposed models show state-of-the-art results in semi-supervised classification tasks across datasets like MNIST, achieving an error rate of 0.96% with a limited number of labeled samples.
3. End-to-End Trainability: Both the ADGM and SDGM are trained end-to-end, eliminating the need for pre-training or manual feature engineering, which is often required in other semi-supervised techniques.
4. Generative Log-Likelihood Performance: The models also excel in unsupervised settings, with competitive performance in log-likelihood evaluations for permutation invariant data.

Implications and Future Directions

The introduction of auxiliary variables marks a significant step forward in constructing more flexible and expressive probabilistic models. This approach provides a substantial groundwork for future research in the area of deep generative models.

Practically, these advancements can be pivotal for applications requiring accurate modeling of complex data distributions with limited labeled data, such as in the fields of bioinformatics and natural language processing.

Theoretically, this framework opens new perspectives on incorporating additional computational mechanisms into the variational inference process. Future research may explore different types of latent variable distributions or investigate the impact of auxiliary models in broader domains, potentially leading to richer and more nuanced generative modeling capabilities.

Overall, the paper contributes meaningful insights and methods to the progression of semi-supervised learning and deep generative modeling.