  Few-shot learning (FSL) has attracted increasing attention in recent years
but remains challenging, due to the intrinsic difficulty in learning to
generalize from a few examples. This paper proposes an adaptive margin
principle to improve the generalization ability of metric-based meta-learning
approaches for few-shot learning problems. Specifically, we first develop a
class-relevant additive margin loss, where semantic similarity between each
pair of classes is considered to separate samples in the feature embedding
space from similar classes. Further, we incorporate the semantic context among
all classes in a sampled training task and develop a task-relevant additive
margin loss to better distinguish samples from different classes. Our adaptive
margin method can be easily extended to a more realistic generalized FSL
setting. Extensive experiments demonstrate that the proposed method can boost
the performance of current metric-based meta-learning approaches, under both
the standard FSL and generalized FSL settings.
