Toward Robust Semi-Supervised Distribution Alignment Against Label Distribution Shift With Noisy Annotations

Deep learning-based AI models typically require a large amount of high-quality annotated data to achieve optimal performance. However, the label distribution shift caused by noisy annotations can lead to perturbations in the classification boundary, reducing the robustness and generalization capabilities of deep learning models. To mitigate this issue, we transform the problem of learning from noisy labels into a semi-supervised learning problem, and propose a novel Semi-Supervised Distribution Alignment (SSDA) framework that strategically integrates noise-robust distribution alignment within a unified semi-supervised learning paradigm for combating noisy labels. By leveraging the similarity distribution between historical predictions, the proposed SSDA approach benefits from a flexible multi-historical regression modeling strategy, which aims to identify high-confidence samples/pairs and recalibrate the label shift through pseudo-labels. Furthermore, our approach employs a comprehensive multi-granularity distribution adaptation strategy, incorporating both instance-wise and class-aware distribution alignment to quantitatively minimize semantic discrepancies across different mixed feature domains. In this way, our SSDA approach ultimately achieves more resilient and generalizable performance against label noise, even in the presence of substantial noise. Extensive experiments conducted on multiple simulated and real-world noisy benchmark datasets consistently demonstrate the superiority and effectiveness of our SSDA method compared to existing state-of-the-art baselines.