Model distillation

Topic

Model distillation, also known as knowledge distillation, is a machine learning process where knowledge is transferred from a large, complex model (the teacher) to a smaller, more efficient one (the student). This technique allows the smaller model to approximate the performance of the larger model while requiring significantly less computational power and memory. It is widely used for model compression to deploy deep learning models on resource-constrained devices.