A few-shot generative model should be able to generate data from a distribution by only observing a limited set of examples. In few-shot learning the model is trained on data from many sets from different distributions sharing some underlying properties such as sets of characters from different alphabets or sets of images of different type objects. We study a latent variables approach that extends the Neural Statistician to a fully hierarchical approach with an attention-based point to set-level aggregation. We extend the previous work to iterative data sampling, likelihood-based model comparison, and adaptation-free out of distribution generalization. Our results show that the hierarchical formulation better captures the intrinsic variability within the sets in the small data regime. With this work we generalize deep latent variable approaches to few-shot learning, taking a step towards large-scale few-shot generation with a formulation that readily can work with current state-of-the-art deep generative models.

本文提出了一种名为SCHA-VAE的全层次聚合变分自编码器方法，将当前的潜变量模型扩展到一种更好地捕获小数据集内固有变化、适合深度生成模型的层级形式，实现了远离分布范围归纳的少样本生成数据的目标。

SCHA-VAE: 面向少量样本生成的分层上下文聚合