In recent years, deep neural networks have been successful in both industry and academia, especially for computer vision tasks. The great success of deep learning is mainly due to its scalability to encode large-scale data and to maneuver billions of model parameters. However, it is a challenge to deploy these cumbersome deep models on devices with limited resources, e.g., mobile phones and embedded devices, not only because of the high computational complexity but also the large storage requirements. To this end, a variety of model compression and acceleration techniques have been developed. As a representative type of model compression and acceleration, knowledge distillation effectively learns a small student model from a large teacher model. It has received rapid increasing attention from the community. This paper provides a comprehensive survey of knowledge distillation from the perspectives of knowledge categories, training schemes, teacher-student architecture, distillation algorithms, performance comparison and applications. Furthermore, challenges in knowledge distillation are briefly reviewed and comments on future research are discussed and forwarded.

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k e V and the (1 0 ") 3055-keV levels, betw een the ( ll~) 3 2 4 0 -k e V and the 11<+) 3 1 19-keV levels, and betw een the l l (+) 3 1 19-keV and the (10" ) 3055-keV levels are not given. T he energies are 285, 120, and 65 keV, respectively, as show n in the corrected level schem e below. A rrow sym bols are added to the lines denoting the 258-keV y ray betw een the (1 4 " )46 9 8 -k eV and the (1 4 " )4440-keV levels, the 193-keV y ray betw een the (1 4~)4 440-keV and the (13" )4247-keV levels, and the 345-keV y ray betw een the (13" ) 4247-keV and the (13" ) 3902-keV levels.T he corrections do not affect the results and conclusions o f the original paper.