Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution

TL;DR

This paper introduces a meta knowledge distillation approach for single image super-resolution that employs learnable knowledge representation networks to enhance the transfer of high-frequency details, improving reconstruction quality without added inference complexity.

Contribution

It proposes a model-agnostic meta knowledge distillation framework with learnable knowledge representation networks tailored for super-resolution tasks, emphasizing texture-aware transfer and meta-learning optimization.

Findings

Outperforms existing knowledge representation distillation methods.

Enhances high-frequency detail recovery in super-resolution.

Does not increase inference complexity.

Abstract

Knowledge distillation (KD), which can efficiently transfer knowledge from a cumbersome network (teacher) to a compact network (student), has demonstrated its advantages in some computer vision applications. The representation of knowledge is vital for knowledge transferring and student learning, which is generally defined in hand-crafted manners or uses the intermediate features directly. In this paper, we propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. It provides a more flexible and accurate way to help the teachers transmit knowledge in accordance with the abilities of students via knowledge representation networks (KRNets) with learnable parameters. In order to improve the perception ability of knowledge representation to students' requirements, we propose to solve the transformation process from intermediate outputs to transferred knowledge by employing the student features and the correlation between teacher and student in the KRNets. Specifically, the texture-aware dynamic kernels are generated and then extract texture features to be improved and the corresponding teacher guidance so as to decompose the distillation problem into texture-wise supervision for further promoting the recovery quality of high-frequency details. In addition, the KRNets are optimized in a meta-learning manner to ensure the knowledge transferring and the student learning are beneficial to improving the reconstructed quality of the student. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods, and can help super-resolution algorithms achieve better reconstruction quality without introducing any inference complexity.