Generative Fidelity

Generative fidelity refers to the accuracy and realism of data generated by artificial intelligence models, a crucial aspect impacting the trustworthiness and utility of synthetic data. Current research focuses on improving fidelity using various architectures, including diffusion models and GANs, addressing limitations like mode collapse and achieving better accuracy-fidelity trade-offs through techniques such as improved loss functions and knowledge distillation. High generative fidelity is essential for applications ranging from data augmentation and privacy-preserving AI to creating realistic synthetic datasets for training and testing machine learning models, particularly in sensitive domains like healthcare.