Joint Diffusion

Joint diffusion models are a burgeoning area of machine learning research focused on leveraging the power of diffusion processes to learn and generate data from multiple, interconnected sources. Current research emphasizes developing architectures that effectively capture the joint probability distribution of these diverse data modalities, often employing techniques like Bayesian inference and score-based models to achieve this. This approach is proving valuable across diverse applications, including image and 3D shape reconstruction, graph generation, and medical imaging, where it enables improved data synthesis, enhanced reconstruction quality, and more accurate predictions by exploiting the complementary information inherent in multiple data streams. The resulting improvements in data representation and generation are driving significant advancements in various scientific fields and practical applications.