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.
翻译:微小的分类旨在学习一种模型,该模型能够在只有少数标签样本的情况下将它全面概括为新任务。要使用在实际应用中更加广泛的非标签数据, Ren 等人 等 等。\ shortcite{ren2018meta} 提议一个半监督的几发分类方法,该方法为每个未标签的样本指定一个适当的标签,由人工定义的量度。然而,人工定义的量度无法捕捉数据中的内在属性。在本文中,我们提议了一个\ textbf{S}S}elf- textb{A}adapive 邻居选择策略,该策略选择了不标签的数据,且以高信任度的半透明方式选择了几个基准级的分类。 SALA 的主要新颖之处是任务适应度度度度测量,它可以以端到端的方式学习不同任务的适应度度量度。 SALA的另一个吸引人的特征是渐进式的邻居选择策略,该选择了以高信任度的半级标准数据,通过几个级级级的分类,以SAL- smart-smagy-s stragy-s