  In many domains, relationships between categories are encoded in the
knowledge graph. Recently, promising results have been achieved by
incorporating knowledge graph as side information in hard classification tasks
with severely limited data. However, prior models consist of highly complex
architectures with many sub-components that all seem to impact performance. In
this paper, we present a comprehensive empirical study on graph embedded
few-shot learning. We introduce a graph regularization approach that allows a
deeper understanding of the impact of incorporating graph information between
labels. Our proposed regularization is widely applicable and model-agnostic,
and boosts the performance of any few-shot learning model, including
fine-tuning, metric-based, and optimization-based meta-learning. Our approach
improves the performance of strong base learners by up to 2% on Mini-ImageNet
and 6.7% on ImageNet-FS, outperforming state-of-the-art graph embedded methods.
Additional analyses reveal that graph regularizing models result in a lower
loss for more difficult tasks, such as those with fewer shots and less
informative support examples.
