Unsupervised Degradation

Unsupervised degradation research focuses on improving image and data restoration techniques without relying on paired high-resolution/low-resolution training data, a significant challenge in many applications. Current efforts concentrate on developing implicit degradation models, often employing contrastive learning or diffusion-based approaches, and leveraging techniques like knowledge distillation and metric learning to enhance model robustness and generalization across diverse degradation types. This work is crucial for advancing various fields, including image super-resolution, video restoration, and AIOps failure prediction, by enabling more efficient and adaptable solutions for handling real-world data imperfections.