Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

Abstract

Meta-learning algorithms produce feature extractors which achieve state-of-the-art performance on few-shot classification. While the literature is rich with meta-learning methods, little is known about why the resulting feature extractors perform so well. We develop a better understanding of the underlying mechanics of meta-learning and the difference between models trained using meta-learning and models which are trained classically. In doing so, we introduce and verify several hypotheses for why meta-learned models perform better. Furthermore, we develop a regularizer which boosts the performance of standard training routines for few-shot classification. In many cases, our routine outperforms meta-learning while simultaneously running an order of magnitude faster..

Publication
ICML 2020.

#A data-driven method to reduce training sample size to mitigate careful model tuning required when using transfer learning for Alzheimer’s disease #classification.

#In IEEE Access, 2019.