ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (12): 2573-2584.doi: 10.7544/issn1000-1239.2021.20210999

Special Issue: 2021可解释智能学习方法及其应用专题

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Interpretable Few-Shot Learning with Contrastive Constraint

Zhang Lingling1, Chen Yiwei1, Wu Wenjun1, Wei Bifan1, Luo Xuan1, Chang Xiaojun2, Liu Jun1   

  1. 1(School of Computer Science and Technology, Xian Jiaotong University, Xian 710049)2(School of Computing Technologies, Royal Melbourne Institute of Technology University, Melbourne, Australia 3000)
  • Online:2021-12-01
  • Supported by: 
    This work was supported by the National Key Research and Development Program of China (2020AAA0108800), the National Natural Science Foundation of China (62137002, 61937001, 62176209, 62176207, 62106190, 62050194), the Innovative Research Group of the National Natural Science Foundation of China (61721002), the Innovation Research Team of Ministry of Education (IRT_17R86), the Consulting Research Project of Chinese Academy of Engineering “The Online and Offline Mixed Educational Service System for ‘The Belt and Road’ Training in MOOC China”, China Postdoctoral Science Foundation (2020M683493), the Project of China Knowledge Centre for Engineering Science and Technology, and the Fundamental Research Funds for the Central Universities (xhj032021013-02).

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.

Key words: few-shot learning, interpretable analysis, contrastive learning, local descriptor, image recognition

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