Boosting Semi-Supervised Learning with Dual-Threshold Screening and Similarity Learning

How to effectively utilize unlabeled data for training is a key problem in Semi-Supervised Learning (SSL). Existing SSL methods often consider the unlabeled data whose predictions are beyond a fixed threshold (e.g., 0.95), and discard those less than 0.95. We argue that these discarded data have a large proportion, are of hard sample, and will benefit the model training if used properly. In this paper, we propose a novel method to take full advantage of the unlabeled data, termed DTS-SimL, which includes two core designs: Dual-Threshold Screening and Similarity Learning. Except for the fixed threshold, DTS-SimL extracts another class-adaptive threshold from the labeled data. Such a class-adaptive threshold can screen many unlabeled data whose predictions are lower than 0.95 but over the extracted one for model training. On the other hand, we design a new similar loss to perform similarity learning for all the highly-similar unlabeled data, which can further mine the valuable information from the unlabeled data. Finally, for more effective training of DTS-SimL, we construct an overall loss function by assigning four different losses to four different types of data. Extensive experiments are conducted on five benchmark datasets, including CIFAR-10, CIFAR-100, SVHN, Mini-ImageNet, and DomainNet-Real. Experimental results show that the proposed DTS-SimL achieves state-of-the-art classification accuracy. The code is publicly available at https://github.com/GZHU-DVL/DTS-SimL.