  Few-shot learning aims to learn classifiers for new classes with only a few
training examples per class. Most existing few-shot learning approaches belong
to either metric-based meta-learning or optimization-based meta-learning
category, both of which have achieved successes in the simplified "$k$-shot
$N$-way" image classification settings. Specifically, the optimization-based
approaches train a meta-learner to predict the parameters of the task-specific
classifiers. The task-specific classifiers are required to be
homogeneous-structured to ease the parameter prediction, so the meta-learning
approaches could only handle few-shot learning problems where the tasks share a
uniform number of classes. The metric-based approaches learn one task-invariant
metric for all the tasks. Even though the metric-learning approaches allow
different numbers of classes, they require the tasks all coming from a similar
domain such that there exists a uniform metric that could work across tasks. In
this work, we propose a hybrid meta-learning model called Meta-Metric-Learner
which combines the merits of both optimization- and metric-based approaches.
Our meta-metric-learning approach consists of two components, a task-specific
metric-based learner as a base model, and a meta-learner that learns and
specifies the base model. Thus our model is able to handle flexible numbers of
classes as well as generate more generalized metrics for classification across
tasks. We test our approach in the standard "$k$-shot $N$-way" few-shot
learning setting following previous works and a new realistic few-shot setting
with flexible class numbers in both single-source form and multi-source forms.
Experiments show that our approach can obtain superior performance in all
settings.
