Few-shot learning aims to recognize new categories using very few labeled samples. Although few-shot learning has witnessed promising development in recent years, most existing methods adopt an average operation to calculate prototypes, thus limited by the outlier samples. In this work, we propose a simple yet effective framework for few-shot classification, which can learn to generate preferable prototypes from few support data, with the help of an episodic prototype generator module. The generated prototype is meant to be close to a certain \textit{\targetproto{}} and is less influenced by outlier samples. Extensive experiments demonstrate the effectiveness of this module, and our approach gets a significant raise over baseline models, and get a competitive result compared to previous methods on \textit{mini}ImageNet, \textit{tiered}ImageNet, and cross-domain (\textit{mini}ImageNet $\rightarrow$ CUB-200-2011) datasets.

翻译：少见的学习旨在利用极少的标签样本来识别新的类别。 虽然少见的学习近年来取得了有希望的发展, 但大多数现有方法都采用了一种平均操作来计算原型, 因而受外部样本的限制。 在这项工作中, 我们提出了一个简单而有效的少见分类框架, 它可以在一个偶数原型原型生成模块的帮助下, 从少数支持数据中学习生成更好的原型。 生成的原型意在接近某种\ textit_ targetproto ⁇ , 并且不那么受到外部样本的影响。 广泛的实验证明了这个模块的有效性, 我们的方法在基线模型上取得了显著的提升, 并且取得了与以前在\ textit{mini{mini}IMageNet、\textit{deid}ImaageNet 和跨多域(\textit{mini}ImageNet $\rightrow$ CUB- 200- 2011) 数据集上的方法相比的竞争结果。