While semi-supervised learning (SSL) has yielded promising results, the more
realistic SSL scenario remains to be explored, in which the unlabeled data
exhibits extremely high recognition difficulty, e.g., fine-grained visual
classification in the context of SSL (SS-FGVC). The increased recognition
difficulty on fine-grained unlabeled data spells disaster for pseudo-labeling
accuracy, resulting in poor performance of the SSL model. To tackle this
challenge, we propose Soft Label Selection with Confidence-Aware Clustering
based on Class Transition Tracking (SoC) by reconstructing the pseudo-label
selection process by jointly optimizing Expansion Objective and Shrinkage
Objective, which is based on a soft label manner. Respectively, the former
objective encourages soft labels to absorb more candidate classes to ensure the
attendance of ground-truth class, while the latter encourages soft labels to
reject more noisy classes, which is theoretically proved to be equivalent to
entropy minimization. In comparisons with various state-of-the-art methods, our
approach demonstrates its superior performance in SS-FGVC. Checkpoints and
source code are available at this https URL

基于类别迁移跟踪的带有置信度感知聚类的软标签选择方法提高了半监督学习中细粒度视觉分类的性能。