Improving Meta-Learning Classification with Prototype Enhancement

Meta-learning classification is about training a classifier by remembering some common knowledge from limited labeled examples in a support set. It has attracted more and more attention in machine learning community. Despite the success, existing Meta-learning algorithms usually ignore the relationship between the samples of the support set and the samples of the query set in a task after feature extraction and thus fail to obtain the most discriminative features, which leads to an unreliable class prototype and a low distinguishability of feature representations. In this paper, we propose a novel metric-based meta-learning algorithm called Prototype Enhancement Network (PEN) to tackle the above problems. The proposed algorithm uses: (1) a Task Context-Related (TCR) Module to obtain a more discriminative and context-related feature representation of samples within a task; and (2) a Prototype Reset (PR) module to expands the support set with partially unlabeled query set samples to mitigate the low-data problem and obtain a more representative class prototype. The cooperation of the TCR module and the PR module effectively enhance the reliability and the accuracy of the class prototype. Experiments are conducted on two classification benchmarks (mini-ImageNet and tiered-ImageNet) and the results confirm the effectiveness of PEN compared with other state-of-the-art methods.