The field of Few-Shot Learning (FSL), or learning from very few (typically
$1$ or $5$) examples per novel class (unseen during training), has received a
lot of attention and significant performance advances in the recent literature.
While number of techniques have been proposed for FSL, several factors have
emerged as most important for FSL performance, awarding SOTA even to the
simplest of techniques. These are: the backbone architecture (bigger is
better), type of pre-training on the base classes (meta-training vs regular
multi-class, currently regular wins), quantity and diversity of the base
classes set (the more the merrier, resulting in richer and better adaptive
features), and the use of self-supervised tasks during pre-training (serving as
a proxy for increasing the diversity of the base set). In this paper we propose
yet another simple technique that is important for the few shot learning
performance - a search for a compact feature sub-space that is discriminative
for a given few-shot test task. We show that the Task-Adaptive Feature
Sub-Space Learning (TAFSSL) can significantly boost the performance in FSL
scenarios when some additional unlabeled data accompanies the novel few-shot
task, be it either the set of unlabeled queries (transductive FSL) or some
additional set of unlabeled data samples (semi-supervised FSL). Specifically,
we show that on the challenging miniImageNet and tieredImageNet benchmarks,
TAFSSL can improve the current state-of-the-art in both transductive and
semi-supervised FSL settings by more than $5\%$, while increasing the benefit
of using unlabeled data in FSL to above $10\%$ performance gain.

本文提出了一种名为 “任务自适应特征子空间学习” 的简单技术，可以显著提高少样本学习方案的性能，特别是当一些额外的未标记数据附带用于新的少样本任务时，而且它可以将在半监督 FSL 设置中更多地受益于使用未标记数据到超过 10% 的性能提高。