SST: Self-training with self-adaptive thresholding for semi-supervised learning

Neural networks have demonstrated exceptional performance in supervised learning, benefiting from abundant high-quality annotated data. However, obtaining such data in real-world scenarios is costly and labor-intensive. Semi-supervised learning (SSL) offers a solution to this problem by utilizing a small amount of labeled data along with a large volume of unlabeled data . Recent studies, such as Semi-ViT and Noisy Student, which employ consistency regularization or pseudo-labeling, have demonstrated significant achievements. However, they still face challenges, particularly in accurately selecting sufficient high-quality pseudo-labels due to their reliance on fixed thresholds. Recent methods such as FlexMatch and FreeMatch have introduced flexible or self-adaptive thresholding techniques, greatly advancing SSL research. Nonetheless, their process of updating thresholds at each iteration is deemed time-consuming, computationally intensive, and potentially unnecessary. To address these issues, we propose Self-training with Self-adaptive Thresholding (SST), a novel, effective, and efficient SSL framework. SST integrates with both supervised (Super-SST) and semi-supervised (Semi-SST) learning. SST introduces an innovative Self-Adaptive Thresholding (SAT) mechanism that adaptively adjusts class-specific thresholds based on the model’s learning progress. SAT ensures the selection of high-quality pseudo-labeled data, mitigating the risks of inaccurate pseudo-labels and confirmation bias (where models reinforce their own mistakes during training). Specifically, SAT prevents the model from prematurely incorporating low-confidence pseudo-labels, reducing error reinforcement and enhancing model performance. Extensive experiments demonstrate that SST achieves state-of-the-art performance with remarkable efficiency, generalization, and scalability across various architectures and datasets. Notably, Semi-SST-ViT-Huge achieves the best results on competitive ImageNet-1K SSL benchmarks (no external data), with 80.7%/84.9% Top-1 accuracy using only 1%/10% labeled data. Compared to the fully-supervised DeiT-III-ViT-Huge, which achieves 84.8% Top-1 accuracy using 100% labeled data, our method demonstrates superior performance using only 10% labeled data. This indicates a tenfold reduction in human annotation costs, significantly narrowing the performance disparity between semi-supervised and fully-supervised methods. These advancements pave the way for further innovations in SSL and practical applications where obtaining labeled data is either challenging or costly.