Diffusion Learning

Diffusion learning is a rapidly evolving field leveraging stochastic processes to generate data, primarily images, and solve inverse problems. Current research focuses on improving model efficiency and generalizability, exploring architectures like diffusion probabilistic models and incorporating techniques such as adaptive noise scheduling and hierarchical denoisers to enhance performance across diverse applications. This approach shows promise in various domains, including medical imaging (e.g., reconstructing CT scans from X-rays), 3D object reconstruction, and human pose estimation, offering improved accuracy and efficiency compared to traditional methods.