  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.
