Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
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.
Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
Generative models like DILED integrate core capabilities for diverse data types, offering enhanced performance.
Convergence Analysis of OT-Flow for Sample Generation
Establishing convergence results for OT-Flow in deep generative models.
Generating, Reconstructing, and Representing Discrete and Continuous Data: Generalized Diffusion with Learnable Encoding-Decoding
DILED integrates core capabilities for diverse data types, offering enhanced performance through generalized diffusion with learnable encoding-decoding.
Controlling the Fidelity and Diversity of Deep Generative Models via Pseudo Density Estimation
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.
