Knowledge Distillation: Training Smaller Models from Large Teachers
In the rapidly evolving landscape of machine learning, the tension between model performance and computational efficiency has become increasingly critical. While large neural networks achieve remarkable results across various domains, their substantial computational requirements often make them impractical for deployment in resource-constrained environments such as mobile devices, edge computing systems, or real-time applications. Knowledge distillation … Read more