Few-shot Learning with Meta Metric Learners

TL;DR

This paper introduces meta metric learning, a novel approach combining task-specific metric learners with a meta learner to improve few-shot learning across diverse domains and flexible label settings.

Contribution

The paper proposes a meta metric learning framework that handles unbalanced classes and diverse tasks, overcoming limitations of existing meta and metric learning methods.

Findings

Achieves superior performance in standard few-shot learning tasks.

Effective in multi-domain and flexible label scenarios.

Outperforms existing methods in diverse task settings.

Abstract

Few-shot Learning aims to learn classifiers for new classes with only a few training examples per class. Existing meta-learning or metric-learning based few-shot learning approaches are limited in handling diverse domains with various number of labels. The meta-learning approaches train a meta learner to predict weights of homogeneous-structured task-specific networks, requiring a uniform number of classes across tasks. The metric-learning approaches learn one task-invariant metric for all the tasks, and they fail if the tasks diverge. We propose to deal with these limitations with meta metric learning. Our meta metric learning approach consists of task-specific learners, that exploit metric learning to handle flexible labels, and a meta learner, that discovers good parameters and gradient decent to specify the metrics in task-specific learners. Thus the proposed model is able to handle unbalanced classes as well as to generate task-specific metrics. We test our approach in the `$k$-shot $N$-way' few-shot learning setting used in previous work and new realistic few-shot setting with diverse multi-domain tasks and flexible label numbers. Experiments show that our approach attains superior performances in both settings.