论文标题
细心的细粒结构稀疏性图像恢复
Attentive Fine-Grained Structured Sparsity for Image Restoration
论文作者
论文摘要
近年来，通过开发大型深层模型，图像恢复任务在近年来见证了绩效的巨大提高。尽管表现出色，但深层模型要求的重量计算限制了图像恢复的应用。为了提高限制，需要减少网络的大小，同时保持准确性。最近，N：M结构化修剪似乎是一种有效且实用的修剪方法之一，可以使模型具有准确性约束。但是，它无法说明图像恢复网络不同层的不同计算复杂性和性能要求。为了进一步优化效率和恢复精度之间的权衡，我们提出了一种新型的修剪方法，该方法决定了N：M结构稀疏度的修剪比。关于超分辨率和脱张任务的广泛实验结果证明了我们方法的功效，该方法的表现胜过以前的修剪方法。提议方法的Pytorch实现可在https://github.com/junghunoh/sls_cvpr2r2022上获得。
Image restoration tasks have witnessed great performance improvement in recent years by developing large deep models. Despite the outstanding performance, the heavy computation demanded by the deep models has restricted the application of image restoration. To lift the restriction, it is required to reduce the size of the networks while maintaining accuracy. Recently, N:M structured pruning has appeared as one of the effective and practical pruning approaches for making the model efficient with the accuracy constraint. However, it fails to account for different computational complexities and performance requirements for different layers of an image restoration network. To further optimize the trade-off between the efficiency and the restoration accuracy, we propose a novel pruning method that determines the pruning ratio for N:M structured sparsity at each layer. Extensive experimental results on super-resolution and deblurring tasks demonstrate the efficacy of our method which outperforms previous pruning methods significantly. PyTorch implementation for the proposed methods is available at https://github.com/JungHunOh/SLS_CVPR2022.
