Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
This work provides an incremental improvement for researchers and practitioners in computer vision seeking to compress super-resolution models.
The paper tackles the problem of knowledge distillation for image super-resolution by addressing semantic differences in feature maps between teacher and student networks, resulting in improved efficiency and performance as demonstrated in experiments.
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.