Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle this recently, we find the current procedure and datasets that are used to systematically assess progress in this setting lacking. To address this, we propose Meta-Dataset: a new benchmark for training and evaluating few-shot classifiers that is large-scale, consists of multiple datasets, and presents more natural and realistic tasks. The aim is to measure the ability of state-of-the-art models to leverage diverse sources of data to achieve higher generalization, and to evaluate that generalization ability in a more challenging setting. We additionally measure robustness of current methods to variations in the number of available examples and the number of classes. Finally our extensive empirical evaluation leads us to identify weaknesses in Prototypical Networks and MAML, two popular few-shot classification methods, and to propose a new method, Proto-MAML, which achieves improved performance on our benchmark.

提出了一个名为Meta-Dataset的大规模基准数据集，用于训练和评估模型在少样本分类问题上的性能，并探讨模型对不同训练来源的泛化能力和元学习的好处。

Meta-Dataset: 从少量示例中学习的数据集