What is knowledge distillation in the context of deep learning?

Knowledge distillation is a technique in deep learning where a smaller model (the student) is trained to replicate the behavior of a larger, pre-trained model (the teacher). This process typically involves training the student to match the teacher’s output probabilities across a dataset. The benefits include creating more efficient models that have lower latency and smaller memory footprints, making them suitable for deployment in resource-constrained environments while maintaining comparable performance to the original model.