Few-Shot Learning (FSL) aims at recognizing the target classes that only a few samples are available for training. The current approaches mostly address FSL by learning a generalized class-level metric while neglect the intra-class distribution information. In this work, we propose Improved Prototypical Networks (IPN) to address this issue. Inspired by the observation that the intra-class samples differ greatly in revealing the class distribution, we first propose an attention-analogous strategy to explore the class distribution information by distributing different weights to samples based on their representativeness. Besides, to further explore the discriminative information across classes, we propose a distance scaling strategy to reduce the intra-class difference while enlarge the inter-class difference. The experimental results on two benchmark datasets show the superiority of the proposed model against the state-of-the-art approaches.

少量学习（FSL）旨在识别仅几个样本可用于训练的目标类别。当前的方法主要通过学习通用的类级别指标来解决FSL，而忽略了类内分发信息。在这项工作中，我们提出了改进的原型网络（IPN）以解决此问题。通过观察类内样本在揭示类分布方面存在很大差异的观察结果，我们首先提出了一种关注类策略，通过基于样本的代表性对样本分配不同的权重来探索类分布信息。此外，为了进一步探讨跨类别的判别信息，我们提出了一种距离缩放策略，以减少类别内差异，同时扩大类别间差异。