On the Quantitative Analysis of Decoder-Based Generative Models

Understand

The past several years have seen remarkable progress in generative models which produce convincing samples of images and other modalities.

• A shared component of many powerful generative models is a decoder network, a parametric deep neural net that defines a generative distribution.
• Examples include variational autoencoders, generative adversarial networks, and generative moment matching networks.
• 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.