Ensemble Knowledge Distillation

Ensemble knowledge distillation improves the efficiency and performance of smaller machine learning models ("students") by leveraging the collective knowledge of multiple larger, more complex models ("teachers"). Current research focuses on optimizing this knowledge transfer process, exploring techniques like weighted averaging of teacher predictions, incorporating both labeled and unlabeled data, and utilizing diverse teacher model architectures to enhance student performance across various tasks, including speech recognition, image classification, and medical diagnosis. This approach offers significant advantages in resource-constrained environments and enables the deployment of powerful models on devices with limited computational capabilities, impacting fields ranging from healthcare to precision agriculture.