Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

Understand

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.
• We experiment with popular baselines and meta-learners on Meta-Dataset, along with a competitive method that we propose.