On the Quantitative Analysis of Decoder-Based Generative Models

Yuhuai Wu, Yuri Burda, Ruslan Salakhutdinov, Roger Grosse

Abstract: The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities. A shared component of some popular models such as generative adversarial networks and generative moment matching networks, is a decoder network, a parametric deep neural net that defines a generative distribution. Unfortunately, it can be difficult to quantify the performance of these models because of the intractability of log-likelihood estimation, and inspecting samples can be misleading. We propose to use Annealed Importance Sampling for evaluating log-likelihoods for decoder-based models and validate its accuracy using bidirectional Monte Carlo. Using this technique, we analyze the performance of decoder-based models, the effectiveness of existing log-likelihood estimators, the degree of overfitting, and the degree to which these models miss important modes of the data distribution.

TL;DR: We propose to use Annealed Importance Sampling to evaluate decoder-based generative network, and investigate various properties of these models.

Conflicts: cs.toronto.edu, cs.cmu.edu, umontreal.ca, ttic.edu, openai.com

Keywords: Deep learning, Unsupervised Learning

Community Implementations: [ 5 code implementations](https://www.catalyzex.com/paper/arxiv:1611.04273/code)