Distillation Paradigm

Knowledge distillation is a machine learning paradigm focused on transferring knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model. Current research emphasizes improving the distillation process itself, exploring techniques like dimensionality reduction, asymmetric distillation, and adaptive weighting of teacher knowledge to enhance student performance and generalization across diverse tasks and model architectures (e.g., convolutional neural networks, transformers, and multi-layer perceptrons). This approach is significant for deploying advanced deep learning models on resource-constrained devices and improving the efficiency of various applications, including anomaly detection, object detection, and recommendation systems.