Prior Distillation

Prior distillation in machine learning focuses on transferring knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, improving the student's performance and reducing computational costs. Current research explores diverse applications, including image generation, physiological signal processing, and 3D object detection, employing techniques like feature distillation, self-similarity learning, and angular margin-based methods within various architectures such as diffusion models and GANs. This approach is significant for enabling deployment of powerful models on resource-constrained devices and improving the efficiency and generalizability of machine learning systems across numerous domains.