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arXiv · 2404.02573

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

Simiao Li·Yun Zhang·Wei Li·Hanting Chen·Wenjia Wang·Bingyi Jing·Shaohui Lin·Jie Hu
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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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Simiao Li, Yun Zhang, Wei Li, Hanting Chen, Wenjia Wang, Bingyi Jing, Shaohui Lin, Jie Hu. 2024-04-03. Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution. https://arxiv.org/abs/2404.02573

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