KGGLDM : Knowledge Graph Guided Diffusion Models for advanced learning

Abstract

This thesis explores a novel approach by bridging the gap of diffusion modeling and knowledge graphs, unveiling a potentially groundbreaking direction that serves as the central theme of this work. We propose incorporating knowledge graph guidance into LDM models to augment precise control over sample generation using domain conceptual knowledge.