Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purpose of training an adaptable model that can then learn classes from new datasets using only a few examples. To this end, we propose to utilize the diverse training set to construct a universal template: a partial model that can define a wide array of dataset-specialized models, by plugging in appropriate components. For each new few-shot classification problem, our approach therefore only requires inferring a small number of parameters to insert into the universal template. We design a separate network that produces an initialization of those parameters for each given task, and we then fine-tune its proposed initialization via a few steps of gradient descent. Our approach is more parameter-efficient, scalable and adaptable compared to previous methods, and achieves the state-of-the-art on the challenging Meta-Dataset benchmark.

本文提出了一种少样本数据集泛化的方法，建立了一种通用模板，可以插入适当的组件定义各种数据集特化模型，通过梯度下降逐步微调自适应模型的参数初始化，相比之前的方法，该方法更具参数效率、可扩展性和适应性，在富有挑战性的Meta-Dataset基准测试上实现了最佳效果。

学习少样本数据泛化的通用模板