论文标题
具有标准化流量解码器的变异自动编码器
Variational Autoencoders with Normalizing Flow Decoders
论文作者
论文摘要
最近提出的标准化流量模型（例如发光）已被证明能够以相对快速的采样速度生成高质量的高维图像。但是，由于它们固有的限制性架构，因此有必要过度深入以有效训练。在本文中，我们建议将发光与基本变化自动编码器相结合，以抵消此问题。我们证明，我们提出的模型在图像质量和测试可能性方面具有竞争力，同时需要更少的训练时间。
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.
