  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.
