Pseudo-loss Confidence Metric for Semi-supervised Few-shot Learning

Kai Huang, Jie Geng, Wen Jiang, Xinyang Deng, Zhe Xu

科研成果: 书/报告/会议事项章节会议稿件同行评审

53 引用 (Scopus)

摘要

Semi-supervised few-shot learning is developed to train a classifier that can adapt to new tasks with limited labeled data and a fixed quantity of unlabeled data. Most semi-supervised few-shot learning methods select pseudo-labeled data of unlabeled set by task-specific confidence estimation. This work presents a task-unified confidence estimation approach for semi-supervised few-shot learning, named pseudo-loss confidence metric (PLCM). It measures the data credibility by the loss distribution of pseudo-labels, which is synthetical considered multi-tasks. Specifically, pseudo-labeled data of different tasks are mapped to a unified metric space by mean of the pseudo-loss model, making it possible to learn the prior pseudo-loss distribution. Then, confidence of pseudo-labeled data is estimated according to the distribution component confidence of its pseudo-loss. Thus highly reliable pseudo-labeled data are selected to strengthen the classifier. Moreover, to overcome the pseudo-loss distribution shift and improve the effectiveness of classifier, we advance the multi-step training strategy coordinated with the class balance measures of class-apart selection and class weight. Experimental results on four popular benchmark datasets demonstrate that the proposed approach can effectively select pseudo-labeled data and achieve the state-of-the-art performance.

源语言英语
主期刊名Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
出版商Institute of Electrical and Electronics Engineers Inc.
8651-8660
页数10
ISBN(电子版)9781665428125
DOI
出版状态已出版 - 2021
活动18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, 加拿大
期限: 11 10月 202117 10月 2021

出版系列

姓名Proceedings of the IEEE International Conference on Computer Vision
ISSN(印刷版)1550-5499

会议

会议18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
国家/地区加拿大
Virtual, Online
时期11/10/2117/10/21

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