The author introduces a new approach called DILED that integrates the core capabilities of generation, reconstruction, and representation for diverse data types. By incorporating parameterized encoding-decoding into the diffusion process, DILED aims to enhance performance and applicability across various models.
Generative models like DILED integrate core capabilities for diverse data types, offering enhanced performance.
Establishing convergence results for OT-Flow in deep generative models.
DILED integrates core capabilities for diverse data types, offering enhanced performance through generalized diffusion with learnable encoding-decoding.
This research paper introduces a novel metric called "pseudo density" to control the fidelity (realism) and diversity (variety) of images generated by deep generative models like GANs and diffusion models.