论文标题
超级分辨率的像素级别的自定进度学习
Pixel-Level Self-Paced Learning for Super-Resolution
论文作者
论文摘要
最近，由于其在基于图像的几个字段中广泛使用，因此提出了许多深网以提高预测超分辨率（SR）图像的质量。但是，随着这些网络的构建越来越深，它们的培训时间也越来越长，这可能指导学习者进行本地优化。为了解决这个问题，本文设计了一种名为Pixel级自定进度学习（PSPL）的培训策略，以加速SISR模型的收敛速度。 PSPL模仿自定进定的学习使预测的SR图像中的每个像素及其在地面真理中的相应像素作为注意力的重量，以指导模型进入参数空间中更好的区域。广泛的实验证明，PSPL可以加快SISR模型的训练，并促使几种现有模型获得新的更好的结果。此外，源代码可在https://github.com/elin24/pspl上获得。
Recently, lots of deep networks are proposed to improve the quality of predicted super-resolution (SR) images, due to its widespread use in several image-based fields. However, with these networks being constructed deeper and deeper, they also cost much longer time for training, which may guide the learners to local optimization. To tackle this problem, this paper designs a training strategy named Pixel-level Self-Paced Learning (PSPL) to accelerate the convergence velocity of SISR models. PSPL imitating self-paced learning gives each pixel in the predicted SR image and its corresponding pixel in ground truth an attention weight, to guide the model to a better region in parameter space. Extensive experiments proved that PSPL could speed up the training of SISR models, and prompt several existing models to obtain new better results. Furthermore, the source code is available at https://github.com/Elin24/PSPL.
