Domain Knowledge Transfer

Domain knowledge transfer focuses on leveraging knowledge learned in one domain (e.g., a specific type of medical data or a particular robotic task) to improve performance in a related but different domain where data is scarce or expensive to obtain. Current research emphasizes techniques like adversarial training, mutual information optimization, and prototype-based methods, often integrated with large language models or graph neural networks, to facilitate robust and effective knowledge transfer across diverse data distributions. This field is crucial for addressing data limitations in various applications, from healthcare and robotics to natural language processing and industrial prognostics, enabling the development of more efficient and generalizable AI systems.