Domain Bridging

Domain bridging in machine learning focuses on adapting models trained on one data domain (source) to perform well on another (target), often without extensive retraining on the target domain. Current research emphasizes techniques like knowledge distillation from powerful "teacher" models (e.g., using Segment Anything Model) to smaller, more efficient "student" models, and the development of novel architectures that facilitate gradual domain alignment through intermediate representations or bridging domains. These advancements are crucial for improving the robustness and generalizability of machine learning models across diverse real-world applications, such as image segmentation and avatar generation, where data scarcity or significant domain shifts are common challenges.