Self-Adaptive Label Augmentation for Semi-supervised Few-shot Classification
This work addresses the challenge of leveraging unlabeled data in few-shot learning, which is crucial for real-world applications where labeled data is scarce, representing an incremental improvement over prior methods.
The paper tackles the problem of semi-supervised few-shot classification by proposing SALA, a method that uses a task-adaptive metric and progressive neighbor selection to assign labels to unlabeled data, resulting in outperforming state-of-the-art methods on benchmark datasets.
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.