  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.
