Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
This work addresses the practical deployment challenge of super-resolution models on resource-limited devices, representing an incremental improvement through a novel distillation method.
The paper tackles the problem of reducing memory and computational costs in super-resolution models for deployment on resource-limited devices by proposing a contrastive self-distillation framework, achieving effective compression and acceleration of standard models like EDSR, RCAN, and CARN with improved PSNR/SSIM.
Convolutional neural networks (CNNs) are highly successful for super-resolution (SR) but often require sophisticated architectures with heavy memory cost and computational overhead, significantly restricts their practical deployments on resource-limited devices. In this paper, we proposed a novel contrastive self-distillation (CSD) framework to simultaneously compress and accelerate various off-the-shelf SR models. In particular, a channel-splitting super-resolution network can first be constructed from a target teacher network as a compact student network. Then, we propose a novel contrastive loss to improve the quality of SR images and PSNR/SSIM via explicit knowledge transfer. Extensive experiments demonstrate that the proposed CSD scheme effectively compresses and accelerates several standard SR models such as EDSR, RCAN and CARN. Code is available at https://github.com/Booooooooooo/CSD.