Classifier Guided Diffusion

Classifier-guided diffusion models leverage the power of diffusion models for generative tasks by incorporating classifier guidance to control the generation process. Current research focuses on applying this framework to diverse problems, including robot design, image generation, and protein design, often employing denoising diffusion models and optimizing for improved generation quality and robustness. This approach offers a powerful and flexible method for generating high-quality samples conditioned on specific attributes or objectives, impacting fields ranging from robotics and materials science to healthcare and computer vision.