Variational Autoencoders with Normalizing Flow Decoders
This work addresses a computational bottleneck in generative modeling for image synthesis, though it is incremental as it builds on existing methods.
The paper tackles the issue of normalizing flow models like Glow requiring excessive depth for effective training by combining Glow with a variational autoencoder, resulting in competitive image quality and test likelihood with far less training time.
Recently proposed normalizing flow models such as Glow have been shown to be able to generate high quality, high dimensional images with relatively fast sampling speed. Due to their inherently restrictive architecture, however, it is necessary that they are excessively deep in order to train effectively. In this paper we propose to combine Glow with an underlying variational autoencoder in order to counteract this issue. We demonstrate that our proposed model is competitive with Glow in terms of image quality and test likelihood while requiring far less time for training.