Denoising Score

Denoising score methods leverage the gradient of a data distribution's density to perform various tasks, primarily focusing on removing noise and improving data quality. Current research emphasizes applications in image and 3D model editing, reinforcement learning, and generative modeling, often employing diffusion models and score-based generative models with techniques like score distillation and contrastive learning to enhance performance and stability. These advancements have significant implications for improving the quality of generated content, enhancing the robustness of machine learning algorithms, and enabling more accurate analysis of complex data, such as atomic structures.