DOmain Label

Domain label research focuses on adapting machine learning models to new data distributions lacking labeled examples, a crucial challenge in various applications. Current efforts concentrate on developing robust methods that leverage unlabeled target data, often employing techniques like contrastive learning, generative models, and self-supervised learning to improve model generalization and mitigate the impact of noisy pseudo-labels. This work is significant because it addresses the limitations of traditional supervised learning in scenarios with limited or unavailable labeled data, enabling more reliable and adaptable AI systems across diverse domains.