Implicit Diffusion

Implicit diffusion models represent a powerful new class of generative models that learn probability distributions by optimizing over the parameters of stochastic diffusion processes. Current research focuses on improving the efficiency and scalability of these models, exploring architectures like implicit neural representations and incorporating techniques such as bilevel optimization and normalizing flows to achieve better performance in tasks such as image super-resolution and large-scale scene representation. This approach offers advantages in generating high-fidelity samples and handling complex data, impacting fields like computer vision and time-series analysis through improved generative capabilities and efficient sampling methods.