Local-Selective Feature Distillation for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2111.10988v1
• Date: Mon, 22 Nov 2021 05:05:37 GMT
• Title: Local-Selective Feature Distillation for Single Image Super-Resolution
• Authors: SeongUk Park, Nojun Kwak
• Abstract summary: We propose a novel feature distillation (FD) method which is suitable for single image super-resolution (SISR) We show the limitations of the existing FitNet-based FD method that it suffers in the SISR task, and propose to modify the existing FD algorithm to focus on local feature information. We call our method local-selective feature distillation (LSFD) and verify that our method outperforms conventional FD methods in SISR problems.
• Score: 42.83228585332463
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent improvements in convolutional neural network (CNN)-based single image super-resolution (SISR) methods rely heavily on fabricating network architectures, rather than finding a suitable training algorithm other than simply minimizing the regression loss. Adapting knowledge distillation (KD) can open a way for bringing further improvement for SISR, and it is also beneficial in terms of model efficiency. KD is a model compression method that improves the performance of Deep Neural Networks (DNNs) without using additional parameters for testing. It is getting the limelight recently for its competence at providing a better capacity-performance tradeoff. In this paper, we propose a novel feature distillation (FD) method which is suitable for SISR. We show the limitations of the existing FitNet-based FD method that it suffers in the SISR task, and propose to modify the existing FD algorithm to focus on local feature information. In addition, we propose a teacher-student-difference-based soft feature attention method that selectively focuses on specific pixel locations to extract feature information. We call our method local-selective feature distillation (LSFD) and verify that our method outperforms conventional FD methods in SISR problems.

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