论文标题
VQ-Draw：顺序离散VAE
VQ-DRAW: A Sequential Discrete VAE
论文作者
论文摘要
在本文中，我提出了VQ-Draw，这是一种用于学习数据的紧凑离散表示的算法。 VQ-Draw利用矢量量化效果，以使DRAW的顺序生成方案适应离散的潜在变量。我表明，VQ-Draw可以有效地学习从各种常见数据集中压缩图像，并在没有自动回归先验的帮助的情况下从这些数据集中生成逼真的样本。
In this paper, I present VQ-DRAW, an algorithm for learning compact discrete representations of data. VQ-DRAW leverages a vector quantization effect to adapt the sequential generation scheme of DRAW to discrete latent variables. I show that VQ-DRAW can effectively learn to compress images from a variety of common datasets, as well as generate realistic samples from these datasets with no help from an autoregressive prior.
