Teacher Student Distillation

Teacher-student distillation is a model compression technique where a smaller "student" network learns from a larger, more complex "teacher" network, aiming to achieve comparable performance with improved efficiency. Current research focuses on optimizing the distillation process by addressing issues like sample selection (e.g., focusing on "hard" samples or relative difficulty), improving the transfer of knowledge across different architectures (e.g., GNNs to MLPs, diffusion models), and enhancing the robustness and generalization of the student model through techniques such as self-distillation and sharpness-aware minimization. This approach is significant for reducing computational costs and improving the deployment of large models in resource-constrained environments, impacting various fields including image generation, natural language processing, and robotics.