Denoising Diffusion

Denoising diffusion models are a class of generative models that learn to reverse a diffusion process, gradually removing noise from a data sample to generate new data points. Current research focuses on applying these models to diverse tasks, including image generation, enhancement, and editing, as well as drug design, medical image analysis, and time series generation, often integrating them with other architectures like transformers and GANs to improve efficiency and performance. This approach offers a powerful tool for generating high-quality data in various domains, leading to advancements in fields ranging from materials science to healthcare through improved data augmentation, anomaly detection, and image analysis. The inherent stochasticity of these models is also being investigated for its role in robustness against adversarial attacks.