Denoising Diffusion Model

Denoising diffusion models (DDMs) are generative models that learn to reverse a noise diffusion process, enabling the generation of high-quality samples from complex data distributions. Current research focuses on improving efficiency through techniques like post-training quantization and developing novel architectures such as directly denoising models and those incorporating neural cellular automata to handle large-scale or high-dimensional data. DDMs are proving valuable across diverse applications, including image and audio generation, medical image analysis (e.g., inpainting and anomaly detection), and even solving inverse problems in areas like remote sensing and cosmology, demonstrating their broad impact on various scientific fields.