Few-shot Learning with Meta Metric Learners

Understand

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.