论文标题
使用领域知识的自我监督回归学习：改善成像中的自我监管的denoising的应用
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
论文作者
论文摘要
预测连续数量的回归是使用计算成像和计算机视觉技术应用的核心部分。然而，研究和理解对回归任务的自我监督学习 - 除了特定的回归任务，图像降级 - 落后于图像。本文提出了一个普遍的自我监督回归学习（SSRL）框架，该框架可以通过使用可设计的伪预测器来封装对特定应用程序的域知识，使学习回归神经网络仅具有输入数据（但没有地面真实目标数据）。本文通过表明在不同的设置下，更好的伪宣言可以使SSRL的特性更接近普通监督学习的属性来强调使用领域知识的重要性。低剂量计算机断层扫描和摄像机图像的数值实验表明，提出的SSRL显着改善了几种现有的自我监督的脱氧方法的质量。
Regression that predicts continuous quantity is a central part of applications using computational imaging and computer vision technologies. Yet, studying and understanding self-supervised learning for regression tasks - except for a particular regression task, image denoising - have lagged behind. This paper proposes a general self-supervised regression learning (SSRL) framework that enables learning regression neural networks with only input data (but without ground-truth target data), by using a designable pseudo-predictor that encapsulates domain knowledge of a specific application. The paper underlines the importance of using domain knowledge by showing that under different settings, the better pseudo-predictor can lead properties of SSRL closer to those of ordinary supervised learning. Numerical experiments for low-dose computational tomography denoising and camera image denoising demonstrate that proposed SSRL significantly improves the denoising quality over several existing self-supervised denoising methods.
