SelfNet: A semi-supervised local Fisher discriminant network for few-shot learning

Abstract

Few-shot learning, employing small-scale labeled samples to recognize new objects, has received substantial research interest. The prototypical network (ProtoNet) is a simple yet effective meta-learning method to solve this problem. In the few-shot scenario, however, the scarcity of data usually has a negative impact on the representational ability of prototypes. In this paper, a unique semi-supervised few-shot learning architecture, referred to as Semi-supervised local Fisher discriminant network (SelfNet), which integrates few-shot learning with subspace learning, is proposed. Using the union of the support set and the additional unlabeled set, a feature projection module is constructed to achieve the subspace projection. Additionally, a pseudo-labeling strategy, which adds the unlabeled samples with high prediction confidence to the support set, is employed to refine the original prototypes. Experimental results on two few-shot classification benchmarks demonstrate that SelfNet can achieve superior performance to the state-of-the-arts, indicating the benefits of utilizing unlabeled samples for feature projection.