Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution

• URL: http://arxiv.org/abs/2404.02573v1
• Date: Wed, 3 Apr 2024 08:47:40 GMT
• Title: Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
• Authors: Simiao Li, Yun Zhang, Wei Li, Hanting Chen, Wenjia Wang, Bingyi Jing, Shaohui Lin, Jie Hu,
• Abstract summary: This work presents MiDPK, a multi-granularity mixture of prior KDPK framework, to facilitate efficient image super-resolution model. Experiments demonstrate the effectiveness of the proposed MiDPK method.
• Score: 25.558550480342614
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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