Gradient Guided Diffusion

Gradient-guided diffusion models leverage the principles of diffusion processes to achieve various objectives, primarily focusing on optimization and generation tasks. Current research explores applications ranging from trajectory optimization in control systems and diverse behavior generation in reinforcement learning to mitigating privacy vulnerabilities in federated learning and accelerating medical image processing. These methods, often employing neural networks and advanced optimization techniques, are proving valuable for improving efficiency, accuracy, and robustness in diverse fields, particularly where handling complex constraints or noisy data is crucial.