  Despite the success that metric learning based approaches have achieved in
few-shot learning, recent works reveal the ineffectiveness of their episodic
training mode. In this paper, we point out two potential reasons for this
problem: 1) the random episodic labels can only provide limited supervision
information, while the relatedness information between the query and support
samples is not fully exploited; 2) the meta-learner is usually constrained by
the limited contextual information of the local episode. To overcome these
problems, we propose a new Global Relatedness Decoupled-Distillation (GRDD)
method using the global category knowledge and the Relatedness
Decoupled-Distillation (RDD) strategy. Our GRDD learns new visual concepts
quickly by imitating the habit of humans, i.e. learning from the deep knowledge
distilled from the teacher. More specifically, we first train a global learner
on the entire base subset using category labels as supervision to leverage the
global context information of the categories. Then, the well-trained global
learner is used to simulate the query-support relatedness in global
dependencies. Finally, the distilled global query-support relatedness is
explicitly used to train the meta-learner using the RDD strategy, with the goal
of making the meta-learner more discriminative. The RDD strategy aims to
decouple the dense query-support relatedness into the groups of sparse
decoupled relatedness. Moreover, only the relatedness of a single support
sample with other query samples is considered in each group. By distilling the
sparse decoupled relatedness group by group, sharper relatedness can be
effectively distilled to the meta-learner, thereby facilitating the learning of
a discriminative meta-learner. We conduct extensive experiments on the
miniImagenet and CIFAR-FS datasets, which show the state-of-the-art performance
of our GRDD method.
