Few-shot learning (FSL) is a challenging learning problem in which only a few samples are available for each class. Decision interpretation is more important in few-shot classification since there is a greater chance of error than in traditional classification. However, most of the previous FSL methods are black-box models. In this paper, we propose an inherently interpretable model for FSL based on human-friendly attributes. Moreover, we propose an online attribute selection mechanism that can effectively filter out irrelevant attributes in each episode. The attribute selection mechanism improves the accuracy and helps with interpretability by reducing the number of participated attributes in each episode. We propose a mechanism that automatically detects the episodes where the pool of human-friendly attributes are not adequate, and compensates by engaging learned unknown attributes. We demonstrate that the proposed method achieves results on par with black-box few-shot-learning models on four widely used datasets.

翻译：小样本学习（FSL）是一个具有挑战性的学习问题，其中每个类别仅提供少量样本。由于小样本分类比传统分类更容易出错，因此决策解释性在此过程中更为重要。然而，现有的大多数FSL方法均为黑盒模型。本文提出了一种基于人类友好属性的、具有内在可解释性的FSL模型。此外，我们设计了一种在线属性选择机制，能够有效过滤每个情节中的无关属性。该属性选择机制通过减少每个情节中参与的属性数量，提高了准确率并增强了可解释性。我们还提出了一种自动检测机制，当人类友好属性池不足以满足需求时，该机制会引入学习得到的未知属性进行补偿。实验表明，所提方法在四个广泛使用的数据集上取得了与黑盒小样本学习模型相当的结果。