  Knowledge distillation (KD) is a promising yet challenging model compression
technique that transfers rich learning representations from a well-performing
but cumbersome teacher model to a compact student model. Previous methods for
image super-resolution (SR) mostly compare the feature maps directly or after
standardizing the dimensions with basic algebraic operations (e.g. average,
dot-product). However, the intrinsic semantic differences among feature maps
are overlooked, which are caused by the disparate expressive capacity between
the networks. This work presents MiPKD, a multi-granularity mixture of prior KD
framework, to facilitate efficient SR model through the feature mixture in a
unified latent space and stochastic network block mixture. Extensive
experiments demonstrate the effectiveness of the proposed MiPKD method.
