Two Stage Knowledge Distillation

Two-stage knowledge distillation is a model compression technique that improves the efficiency and performance of smaller "student" models by leveraging the knowledge of larger, more powerful "teacher" models in a sequential manner. Research focuses on optimizing this two-stage process, often employing techniques like parameter-efficient fine-tuning and novel loss functions tailored to specific tasks and model architectures (e.g., transformers, convolutional neural networks). This approach is significant because it enables the deployment of high-performing models on resource-constrained devices while reducing training costs and improving generalization across various applications, including natural language processing, computer vision, and speech recognition.