Flow Based Generative

Flow-based generative models aim to learn complex probability distributions by defining a transformation (a "flow") from a simple, known distribution to a target distribution of interest, enabling efficient sampling and density estimation. Current research emphasizes improving the efficiency and scalability of these models, particularly for high-dimensional and multimodal data, with a focus on architectures like normalizing flows and Poisson flows, and algorithms that address challenges such as slow sampling and limited expressiveness. These advancements are impacting diverse fields, including medical imaging (e.g., image reconstruction and generation), astrophysics (e.g., stellar model emulation), and other areas requiring efficient generation of complex data.