Unsupervised Domain

Unsupervised domain adaptation (UDA) focuses on training machine learning models to generalize across different data distributions without relying on labeled data from the target domain. Current research heavily utilizes generative adversarial networks (GANs), diffusion models, and contrastive learning methods, often within teacher-student frameworks or by incorporating self-supervised learning strategies to improve feature alignment and pseudo-label generation. This field is crucial for addressing data scarcity issues in various applications, such as medical image analysis and remote sensing, enabling the development of robust and fair AI systems across diverse datasets and improving the efficiency of model training.