A New Meta-Baseline for Few-Shot Learning

This work presents a Meta-Baseline method, by pre-training a classifier on all base classes and meta-learning on a nearest-centroid based few-shot classification algorithm, which outperforms recent state-of-the-art methods by a large margin.