Knowledge distillation

Machine learning method to transfer knowledge from a large model to a smaller one / From Wikipedia, the free encyclopedia

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In machine learning, knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized. It can be just as computationally expensive to evaluate a model even if it utilizes little of its knowledge capacity. Knowledge distillation transfers knowledge from a large model to a smaller model without loss of validity. As smaller models are less expensive to evaluate, they can be deployed on less powerful hardware (such as a mobile device).[1]

Knowledge distillation has been successfully used in several applications of machine learning such as object detection,[2] acoustic models,[3] and natural language processing.[4] Recently, it has also been introduced to graph neural networks applicable to non-grid data.[5]