Self-supervised regression learning using domain knowledge: Applications to improving self-supervised denoising in imaging
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DenoisingImage DenoisingregressionSelf-Supervised LearningSimilar Papers 제목 키워드 기반
Self-supervised regression learning using domain knowledge: Applications to improving self-supervised image denoising
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 -…
DenoisingImage DenoisingregressionSelf-Supervised LearningRobust Alzheimer's Progression Modeling using Cross-Domain Self-Supervised Deep Learning
Developing successful artificial intelligence systems in practice depends on both robust deep learning models and large, high-quality data. However, acquiring and labeling data can be prohibitively expensive and time-con…
regressionSelf-Supervised LearningWeakly-Supervised Domain Adaptation of Deep Regression Trackers via Reinforced Knowledge Distillation
Deep regression trackers are among the fastest tracking algorithms available, and therefore suitable for real-time robotic applications. However, their accuracy is inadequate in many domains due to distribution shift and…
Domain AdaptationKnowledge DistillationregressionVisual Object TrackingS$^3$R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification
Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate an…
Contrastive Learningimage-classificationImage ClassificationregressionFunctional Knowledge Transfer with Self-supervised Representation Learning
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization …
Representation LearningSelf-Supervised LearningTransfer Learning