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 it, we find the procedure and datasets that are used to assess their progress lacking. To address this limitation, we propose Meta-Dataset: a new benchmark for training and evaluating models that is large-scale, consists of diverse datasets, and presents more realistic tasks.
（Few-shot分类是指仅通过少量示例学习一个针对新类别的分类器。虽然已经出现了许多模型来应对这个问题，但我们发现用于评估这些模型进展的过程和数据集存在不足。为了解决这个限制，我们提出了Meta-Dataset：一个用于训练和评估模型的新基准，它具有大规模性、包含多样数据集，并呈现更加真实的任务。）
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