Knowledge Transfer

Knowledge transfer in machine learning focuses on efficiently leveraging knowledge learned from one task or model (the "teacher") to improve performance on a different task or model (the "student"). Current research emphasizes techniques like knowledge distillation, often employing multi-mentor or student-oriented approaches, and explores diverse methods for aligning and transferring knowledge across different modalities (e.g., image and text) or heterogeneous devices. This field is crucial for improving model efficiency, reducing training costs, and enabling adaptation to new domains and data scarcity, with applications ranging from medical image analysis to robotics and natural language processing.