  Meta-learning extracts common knowledge from learning different tasks and
uses it for unseen tasks. It can significantly improve tasks that suffer from
insufficient training data, e.g., few shot learning. In most meta-learning
methods, tasks are implicitly related by sharing parameters or optimizer. In
this paper, we show that a meta-learner that explicitly relates tasks on a
graph describing the relations of their output dimensions (e.g., classes) can
significantly improve few shot learning. The graph's structure is usually free
or cheap to obtain but has rarely been explored in previous works. We develop a
novel meta-learner of this type for prototype-based classification, in which a
prototype is generated for each class, such that the nearest neighbor search
among the prototypes produces an accurate classification. The meta-learner,
called "Gated Propagation Network (GPN)", learns to propagate messages between
prototypes of different classes on the graph, so that learning the prototype of
each class benefits from the data of other related classes. In GPN, an
attention mechanism aggregates messages from neighboring classes of each class,
with a gate choosing between the aggregated message and the message from the
class itself. We train GPN on a sequence of tasks from many-shot to few shot
generated by subgraph sampling. During training, it is able to reuse and update
previously achieved prototypes from the memory in a life-long learning cycle.
In experiments, under different training-test discrepancy and test task
generation settings, GPN outperforms recent meta-learning methods on two
benchmark datasets. The code of GPN and dataset generation is available at
https://github.com/liulu112601/Gated-Propagation-Net.
