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.
翻译:微小的基因化模型应该能够只通过观察有限的一组例子来从分布中生成数据。 在几个镜头的学习中,该模型就来自不同分布组的许多数据集的数据进行了培训,这些数据集分享了一些基本特性,例如来自不同字母的字符组或不同类型对象的图像组。我们研究了一种潜在的变量方法,将神经统计学家扩大到完全分级,对定级聚合采取基于关注点的完全分级的方法。我们把以前的工作扩大到迭代数据抽样、基于可能性的模式比较和不因分布的通用而适应。我们的结果显示,等级配方更好地捕捉了小数据系统中各组内部的变异性。我们通过这项工作将深潜变异方法推广到几张光学,向大规模几发一代迈出一步,其配方可以随时与目前最先进的深层基因化模型合作。