Semi-supervised learning is attracting blooming attention, due to its success in combining unlabeled data. However, pseudo-labeling-based semi-supervised approaches suffer from two problems in image classification: (1) Existing methods might fail to adopt suitable thresholds since they either use a pre-defined/fixed threshold or an ad-hoc threshold adjusting scheme, resulting in inferior performance and slow convergence. (2) Discarding unlabeled data with confidence below the thresholds results in the loss of discriminating information. To solve these issues, we develop an effective method to make sufficient use of unlabeled data. Specifically, we design a self adaptive threshold pseudo-labeling strategy, which thresholds for each class can be dynamically adjusted to increase the number of reliable samples. Meanwhile, in order to effectively utilise unlabeled data with confidence below the thresholds, we propose an unreliable sample contrastive loss to mine the discriminative information in low-confidence samples by learning the similarities and differences between sample features. We evaluate our method on several classification benchmarks under partially labeled settings and demonstrate its superiority over the other approaches.

自适应阈值伪标注策略和不可靠样本对比损失被提出用于半监督学习中，以解决图像分类中现有方法自定义/固定阈值或自适应调整方案可能导致性能不佳和收敛缓慢的问题，以及舍弃低置信度的未标记数据可能导致信息损失的问题。实验结果表明，该方法在部分标记的设置下优于其他方法。

自适应阈值伪标签和不可靠样本对比损失在半监督图像分类中的应用