论文标题
部分可观测时空混沌系统的无模型预测
Interpretable Concept-based Prototypical Networks for Few-Shot Learning
论文作者
论文摘要
很少有学习旨在识别样本有限的课程的新实例。通常通过对类似任务进行元学习来缓解这项具有挑战性的任务。但是，最终的型号是黑盒。人们对部署黑盒机器学习模型的关注日益加剧，而FSL在这方面并不例外。在本文中，我们提出了一种基于一组人解剖概念的FSL方法。它构建了一组与概念相关的度量空间，并通过汇总特定于概念的决策来分类新类别的样本。提出的方法不需要查询样本的概念注释。这种可解释的方法在幼崽细粒鸟类分类数据集上使用了六种先前先前最先进的黑盒FSL方法的结果。
Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. However, the resulting models are black-boxes. There has been growing concerns about deploying black-box machine learning models and FSL is not an exception in this regard. In this paper, we propose a method for FSL based on a set of human-interpretable concepts. It constructs a set of metric spaces associated with the concepts and classifies samples of novel classes by aggregating concept-specific decisions. The proposed method does not require concept annotations for query samples. This interpretable method achieved results on a par with six previously state-of-the-art black-box FSL methods on the CUB fine-grained bird classification dataset.
