Intermediate Domain

Intermediate domains are synthetic data distributions strategically positioned between source and target domains in domain adaptation tasks, aiming to bridge significant distributional gaps that hinder knowledge transfer. Current research focuses on generating these intermediate domains using techniques like mixup, style transfer, and optimal transport, often integrated into frameworks employing gradual self-training or other iterative adaptation strategies. This approach improves the robustness and effectiveness of domain adaptation across various applications, including medical image segmentation, person re-identification, and 3D semantic segmentation, by facilitating smoother knowledge transfer and mitigating the negative effects of large domain shifts.