Machine learning method to transfer knowledge from a large model to a smaller one
In machine learning, knowledge distillation or model 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]
^Hinton, Geoffrey; Vinyals, Oriol; Dean, Jeff (2015). "Distilling the knowledge in a neural network". arXiv:1503.02531 [stat.ML].
^Chen, Guobin; Choi, Wongun; Yu, Xiang; Han, Tony; Chandraker, Manmohan (2017). "Learning efficient object detection models with knowledge distillation". Advances in Neural Information Processing Systems: 742–751.
^Asami, Taichi; Masumura, Ryo; Yamaguchi, Yoshikazu; Masataki, Hirokazu; Aono, Yushi (2017). Domain adaptation of DNN acoustic models using knowledge distillation. IEEE International Conference on Acoustics, Speech and Signal Processing. pp. 5185–5189.
^Cui, Jia; Kingsbury, Brian; Ramabhadran, Bhuvana; Saon, George; Sercu, Tom; Audhkhasi, Kartik; Sethy, Abhinav; Nussbaum-Thom, Markus; Rosenberg, Andrew (2017). Knowledge distillation across ensembles of multilingual models for low-resource languages. IEEE International Conference on Acoustics, Speech and Signal Processing. pp. 4825–4829.
^Yang, Yiding; Jiayan, Qiu; Mingli, Song; Dacheng, Tao; Xinchao, Wang (2020). "Distilling Knowledge from Graph Convolutional Networks" (PDF). Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 7072–7081. arXiv:2003.10477. Bibcode:2020arXiv200310477Y.
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