This paper tackles the problem of semi-supervised learning when the set of
labeled samples is limited to a small number of images per class, typically
less than 10, problem that we refer to as barely-supervised learning. We
analyze in depth the behavior of a state-of-the-art semi-supervised method,
FixMatch, which relies on a weakly-augmented version of an image to obtain
supervision signal for a more strongly-augmented version. We show that it
frequently fails in barely-supervised scenarios, due to a lack of training
signal when no pseudo-label can be predicted with high confidence. We propose a
method to leverage self-supervised methods that provides training signal in the
absence of confident pseudo-labels. We then propose two methods to refine the
pseudo-label selection process which lead to further improvements. The first
one relies on a per-sample history of the model predictions, akin to a voting
scheme. The second iteratively updates class-dependent confidence thresholds to
better explore classes that are under-represented in the pseudo-labels. Our
experiments show that our approach performs significantly better on STL-10 in
the barely-supervised regime, e.g. with 4 or 8 labeled images per class.

本文针对有限标注信息下的半监督学习进行研究，分析了当前应用最广的半监督学习方法 FixMatch 在这种情况下的表现和局限，提出了一种利用自监督学习方法提供训练信号以及优化伪标签筛选过程的方案，并在 STL-10 数据集上得到了显著提高。