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    基于对比约束的可解释小样本学习

    Interpretable Few-Shot Learning with Contrastive Constraint

    • 摘要: 不同于基于大规模监督的深度学习方法,小样本学习旨在从极少的几个样本中学习这类样本的特性,其更符合人脑的视觉认知机制.近年来,小样本学习受到很多学者关注,他们联合元学习训练模式与度量学习理论,挖掘查询集(无标记样本)和支持集(少量标记样本)在特征空间的语义相似距离,取得不错的小样本分类性能.然而,这些方法的可解释性偏弱,不能为用户提供一种便于直观理解的小样本推理过程.为此,提出一种基于区域注意力机制的小样本分类网络INT-FSL,旨在揭示小样本分类中的2个关键问题:1)图像哪些关键位置的视觉特征在决策中发挥了重要作用;2)这些关键位置的视觉特征能体现哪些类别的特性.除此之外,尝试在每个小样本元任务中设计全局和局部2种对比学习机制,利用数据内部信息来缓解小样本场景中的监督信息匮乏问题.在3个真实图像数据集上进行了详细的实验分析,结果表明:所提方法INT-FSL不仅能有效提升当前小样本学习方法的分类性能,还具备良好的过程可解释性.

       

      Abstract: Different from deep learning with large scale supervision, few-shot learning aims to learn the samples characteristics from a few labeled examples. Apparently, few-shot learning is more in line with the visual cognitive mechanism of the human brain. In recent years, few-shot learning has attracted more researchers attention. In order to discover the semantic similarities between the query set (unlabeled image) and support set (few labeled images) in feature embedding space, methods which combine meta-learning and metric learning have emerged and achieved great performance on few-shot image classification tasks. However, these methods lack the interpretability, which means they could not provide a reasoning explainable process like human cognitive mechanism. Therefore, we propose a novel interpretable few-shot learning method called INT-FSL based on the positional attention mechanism, which aims to reveal two key problems in few-shot classification: 1)Which parts of the unlabeled image play an important role in classification task; 2)Which class of features reflected by the key parts. Besides, we design the contrastive constraints on global and local levels in every few-shot meta task, for alleviating the limited supervision with the internal information of the data. We conduct extensive experiments on three image benchmark datasets. The results show that the proposed model INT-FSL not only could improve the classification performance on few-shot learning effectively, but also has good interpretability in the reasoning process.

       

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