Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors. To achieve efficient inference in this new class of models, we leverage structure in the covariance matrix, and introduce a new stochastic backpropagation strategy that allows for computing stochastic gradients in a distributed and low-memory fashion. We show that our method outperforms conditional VAEs (CVAEs) and an adaptation of standard VAEs in two image data applications.

本文研究了一种新的模型Gaussian Process Prior Variational Autoencoder(GPPVAE)，结合了VAEs和具有GP priors关联特性的能力，解决了VAEs假设的潜在样本表示是独立并且同分布的假设不够强的问题，并利用协方差矩阵中的结构，介绍了一种新的随机反向传播策略，达到了分布式和低内存计算随机梯度的目的，实验证明该方法可以在两个图像数据应用程序中优于conditional VAEs (CVAEs)和标准VAEs。

高斯过程先验变分自编码器