The key issue of few-shot learning is learning to generalize. This paper
proposes a large margin principle to improve the generalization capacity of
metric based methods for few-shot learning...
To realize it, we develop a unified
framework to learn a more discriminative metric space by augmenting the
classification loss function with a large margin distance loss function for
training. Extensive experiments on two state-of-the-art few-shot learning
methods, graph neural networks and prototypical networks, show that our method
can improve the performance of existing models substantially with very little
computational overhead, demonstrating the effectiveness of the large margin
principle and the potential of our method.