Task-Adaptive Feature Reweighting for Few Shot Classification

摘要

Few shot classification remains a quite challenging problem due to lacking data to train an effective classifier. Lately a few works employ the meta learning schema to learn a generalized feature encoder or distance metric, which is directly used for those unseen classes. In these approaches, the feature representation of a class remains the same even in different tasks (In meta learning, a task of few shot classification involves a set of labeled examples (support set) and a set of unlabeled examples (query set) to be classified. The goal is to get a classifier for the classes in the support set.), i.e. the feature encoder cannot adapt to different tasks. As well known, when distinguishing a class from different classes, the most discriminative feature may be different. Following this intuition, this work proposes a task-adaptive feature reweighting strategy within the framework of recently proposed prototypical network [6]. By considering the relationship between classes in a task, our method generates a feature weight for each class to highlight those features that can better distinguish it from the rest ones. As a result, each class has its own specific feature weight, and this weight is adaptively different in different tasks. The proposed method is evaluated on two few shot classification benchmarks, minilmageNet and tieredImageNet. The experiment results show that our method outperforms the state-of-the-art works demonstrating its effectiveness.