Map Distillation

Map distillation is a machine learning technique focused on transferring knowledge from a complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance while reducing computational costs. Current research explores various distillation methods, including those leveraging attention mechanisms, variational inference for diffusion models and mixtures of experts, and hierarchical approaches for handling multi-modal data or complex structures like ICD codes. This technique is significant for improving the efficiency and accessibility of various models across diverse applications, ranging from image recognition and video analysis to generative modeling and medical diagnosis.