Lessons from Chasing Few-Shot Learning Benchmarks: Rethinking the Evaluation of Meta-Learning Methods

This work introduces a simple baseline for meta-learning, and shows that for each of these two goals, current few-shot learning benchmarks have potential pitfalls in performing model selection and hyperparameter tuning for a given meta-learning method and comparing the performance of different meta-learning methods.