  Graph neural networks (GNNs) have been used to tackle the few-shot learning
(FSL) problem and shown great potentials under the transductive setting.
However under the inductive setting, existing GNN based methods are less
competitive. This is because they use an instance GNN as a label
propagation/classification module, which is jointly meta-learned with a feature
embedding network. This design is problematic because the classifier needs to
adapt quickly to new tasks while the embedding does not. To overcome this
problem, in this paper we propose a novel hybrid GNN (HGNN) model consisting of
two GNNs, an instance GNN and a prototype GNN. Instead of label propagation,
they act as feature embedding adaptation modules for quick adaptation of the
meta-learned feature embedding to new tasks. Importantly they are designed to
deal with a fundamental yet often neglected challenge in FSL, that is, with
only a handful of shots per class, any few-shot classifier would be sensitive
to badly sampled shots which are either outliers or can cause inter-class
distribution overlapping. %Our two GNNs are designed to address these two types
of poorly sampled few-shots respectively and their complementarity is exploited
in the hybrid GNN model. Extensive experiments show that our HGNN obtains new
state-of-the-art on three FSL benchmarks.
