Boosting Semi-Supervised Learning with Dual-Threshold Screening and Similarity Learning
Zechen Liang, Yuan‐Gen Wang, Wei Lu, Xiaochun Cao · ACM Transactions on Multimedia Computing Communications and Applications · 2024
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 article, 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 .