Knowledge Enhanced Generative Model

Knowledge-enhanced generative models aim to improve the accuracy and interpretability of generative models by incorporating external knowledge sources, such as knowledge graphs or expert insights. Current research focuses on integrating this knowledge into various generative architectures, including diffusion models and GANs, to address limitations in data availability and improve the alignment between user intent and generated output across diverse domains like medical imaging and drug discovery. This approach holds significant promise for enhancing the performance and reliability of AI systems in various fields, particularly where data is scarce or complex relationships need to be explicitly modeled.