Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification

• URL: http://arxiv.org/abs/2206.08150v1
• Date: Thu, 16 Jun 2022 13:14:03 GMT
• Title: Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
• Authors: Xueliang Wang, Jianyu Cai, Shuiwang Ji, Houqiang Li, Feng Wu, Jie Wang
• Abstract summary: Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. We propose a semi-supervised few-shot classification method that assigns an appropriate label to each unlabeled sample by a manually defined metric. A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion.
• Score: 121.63992191386502
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Few-shot classification aims to learn a model that can generalize well to new tasks when only a few labeled samples are available. To make use of unlabeled data that are more abundantly available in real applications, Ren et al. \shortcite{ren2018meta} propose a semi-supervised few-shot classification method that assigns an appropriate label to each unlabeled sample by a manually defined metric. However, the manually defined metric fails to capture the intrinsic property in data. In this paper, we propose a \textbf{S}elf-\textbf{A}daptive \textbf{L}abel \textbf{A}ugmentation approach, called \textbf{SALA}, for semi-supervised few-shot classification. A major novelty of SALA is the task-adaptive metric, which can learn the metric adaptively for different tasks in an end-to-end fashion. Another appealing feature of SALA is a progressive neighbor selection strategy, which selects unlabeled data with high confidence progressively through the training phase. Experiments demonstrate that SALA outperforms several state-of-the-art methods for semi-supervised few-shot classification on benchmark datasets.

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