Ensemble Distillation

Ensemble distillation is a machine learning technique that improves the efficiency and performance of complex models by transferring the knowledge from an ensemble of larger, more computationally expensive "teacher" models to a smaller, more efficient "student" model. Current research focuses on applying this technique across diverse domains, including computer vision, natural language processing, and robotics, often employing transformer-based architectures and leveraging techniques like logit-based distillation and adaptive knowledge integration to optimize the knowledge transfer process. This approach offers significant benefits by enabling the deployment of high-performing models on resource-constrained devices while maintaining or even exceeding the accuracy of the original ensembles, thus impacting various fields requiring efficient and accurate prediction models.