Diversity with Cooperation: Ensemble Methods for Few-Shot Classification

Understand

Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples.

• To solve this challenging problem, meta-learning has become a popular paradigm that advocates the ability to "learn to adapt".
• Recent works have shown, however, that simple learning strategies without meta-learning could be competitive.
• In this paper, we go a step further and show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform current meta-learning techniques.