  Few-shot learning is an important area of research. Conceptually, humans are
readily able to understand new concepts given just a few examples, while in
more pragmatic terms, limited-example training situations are common in
practice. Recent effective approaches to few-shot learning employ a
metric-learning framework to learn a feature similarity comparison between a
query (test) example, and the few support (training) examples. However, these
approaches treat each support class independently from one another, never
looking at the entire task as a whole. Because of this, they are constrained to
use a single set of features for all possible test-time tasks, which hinders
the ability to distinguish the most relevant dimensions for the task at hand.
In this work, we introduce a Category Traversal Module that can be inserted as
a plug-and-play module into most metric-learning based few-shot learners. This
component traverses across the entire support set at once, identifying
task-relevant features based on both intra-class commonality and inter-class
uniqueness in the feature space. Incorporating our module improves performance
considerably (5%-10% relative) over baseline systems on both mini-ImageNet and
tieredImageNet benchmarks, with overall performance competitive with recent
state-of-the-art systems.
