BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling (1902.02102v3)

Abstract: With the introduction of the variational autoencoder (VAE), probabilistic latent variable models have received renewed attention as powerful generative models. However, their performance in terms of test likelihood and quality of generated samples has been surpassed by autoregressive models without stochastic units. Furthermore, flow-based models have recently been shown to be an attractive alternative that scales well to high-dimensional data. In this paper we close the performance gap by constructing VAE models that can effectively utilize a deep hierarchy of stochastic variables and model complex covariance structures. We introduce the Bidirectional-Inference Variational Autoencoder (BIVA), characterized by a skip-connected generative model and an inference network formed by a bidirectional stochastic inference path. We show that BIVA reaches state-of-the-art test likelihoods, generates sharp and coherent natural images, and uses the hierarchy of latent variables to capture different aspects of the data distribution. We observe that BIVA, in contrast to recent results, can be used for anomaly detection. We attribute this to the hierarchy of latent variables which is able to extract high-level semantic features. Finally, we extend BIVA to semi-supervised classification tasks and show that it performs comparably to state-of-the-art results by generative adversarial networks.

• The paper introduces BIVA, a Bidirectional-Inference Variational Autoencoder, which uses a very deep hierarchy of latent variables and a novel bidirectional inference network to improve generative modeling.
• BIVA achieves state-of-the-art test likelihoods and high sample quality on datasets like CIFAR-10, closing the performance gap with autoregressive and flow-based models.
• The model demonstrates strong performance in anomaly detection and semi-supervised learning by effectively utilizing its deep hierarchical structure to capture high-level semantic features.

An Academic Discussion of BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling

The paper "BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling" addresses the limitations observed in existing generative models, particularly Variational Autoencoders (VAEs), by proposing an extension called the Bidirectional-Inference Variational Autoencoder (BIVA). This model aims to close the performance gap between VAEs and other powerful generative frameworks like autoregressive and flow-based models by leveraging a sophisticated hierarchy of latent variables and a novel inference mechanism.

Innovations in BIVA Architecture

BIVA distinguishes itself through the integration of a deep hierarchy of stochastic latent variables, enhanced with skip-connections and a bidirectional inference network. The model's architecture is designed to overcome the issues of latent variable collapse typically observed in standard VAEs, especially when the hierarchy is deep. The authors propose two key improvements:

1. Skip-Connected Generative Model: BIVA introduces skip connections within its architecture, similar to techniques employed in ResNet, to facilitate the flow of information and mitigate gradient vanishing, fostering a more active use of all latent variables.
2. Bidirectional Inference Network: Unlike traditional VAEs that use a bottom-up inference approach, BIVA employs a bidirectional strategy that incorporates both bottom-up and top-down paths. This dual-pathway inference network uses stochastic variables in both directions, enhancing the expressiveness of the posterior approximation and allowing for more complex covariance structures.

Empirical Results

The empirical analysis demonstrates BIVA's significant performance improvements. Notably, on benchmark datasets like CIFAR-10, BIVA achieves competitive state-of-the-art results in terms of test likelihoods and shows superior sample generation quality compared to existing non-autoregressive models. Moreover, the model effectively utilizes its hierarchical latent structure for anomaly detection—a task where previous models often fail due to an over-emphasis on low-level data statistics rather than high-level semantic features.

Semi-Supervised Learning and Anomaly Detection

BIVA extends to semi-supervised learning, where it closely rivals contemporary generative adversarial networks (GANs) in classification accuracy. This extension involves incorporating a categorical variable to model classes and a classifier in the inference network, proving its versatility beyond unsupervised settings.

In the context of anomaly detection, BIVA can discern between in-distribution and out-of-distribution data by emphasizing higher-level semantic layers in its hierarchy. This feature fundamentally differentiates BIVA from many state-of-the-art explicit density models that typically fall short on this task.

Implications and Future Directions

BIVA's introduction marks a crucial step in generative modeling, particularly for probabilistic latent variable models, challenging the dominance of autoregressive and flow-based approaches for high-dimensional data generation. It opens avenues for incorporating deep hierarchical structures and enhanced inference mechanisms to make generative models more robust and versatile across tasks.

Potential future developments could investigate more scalable BIVA architectures for even larger hierarchical depths and explore its applications across diverse domains, including complex text generation and audio synthesis. Additionally, research might explore integrating such a sophisticated modeling framework within real-world applications like anomaly detection in complex, high-dimensional settings, ensuring that the captured semantics align closely with operational needs.