Multi Level Distillation

Multi-level distillation in deep learning aims to efficiently transfer knowledge from large, complex "teacher" models to smaller, more efficient "student" models, improving the student's performance without requiring extensive retraining. Current research focuses on refining distillation techniques across various levels of model representation, from individual tokens or pixels to entire sentences or feature maps, and across diverse architectures including Transformers and object detectors. This approach is significant because it enables the deployment of high-performing models on resource-constrained devices and improves the generalization capabilities of smaller models, impacting various applications from natural language processing to computer vision.