Pseudo-labeling is a popular semi-supervised learning technique to leverage
unlabeled data when labeled samples are scarce. The generation and selection of
pseudo-labels heavily rely on labeled data. Existing approaches implicitly
assume that the labeled data is gold standard and 'perfect'. However, this can
be violated in reality with issues such as mislabeling or ambiguity. We address
this overlooked aspect and show the importance of investigating labeled data
quality to improve any pseudo-labeling method. Specifically, we introduce a
novel data characterization and selection framework called DIPS to extend
pseudo-labeling. We select useful labeled and pseudo-labeled samples via
analysis of learning dynamics. We demonstrate the applicability and impact of
DIPS for various pseudo-labeling methods across an extensive range of
real-world tabular and image datasets. Additionally, DIPS improves data
efficiency and reduces the performance distinctions between different
pseudo-labelers. Overall, we highlight the significant benefits of a
data-centric rethinking of pseudo-labeling in real-world settings.

伪标记是一种使用少量标记样本来利用无标记数据的常用半监督学习技术。本研究提出了一种名为 DIPS 的数据特征化和选择框架，通过对学习动态的分析来改善伪标记方法，提高标记数据的质量，并展示了其在各种真实世界的表格和图像数据集上的应用和影响。综上所述，我们强调了在真实环境中数据为中心的伪标记方法的显著优势。