Evaluating Generative Models: A Review of Challenges and Opportunities in Assessment of Generative Adversarial Networks

Generative adversarial networks (GANs) have achieved remarkable success in image synthesis but evaluating their quality remains an open challenge. Existing automated metrics like FID are unable to fully capture generation performance across both objective fidelity and subjective aesthetic attributes important to human perception. This paper conducts an extensive review of literature on GAN evaluation methodology. Key limitations of current approaches are analyzed through a discussion of popular metrics and their inability to predict human judgment comprehensively. To address this, a novel hybrid evaluation model is proposed that combines automated scores with human ratings to provide a more balanced assessment. A pilot study demonstrates the hybrid metric can predict human response more accurately. However, additional validation is required. The review emphasizes the need for evaluation systems integrating richer human input to better optimize models according to user priorities. Interactive frameworks and techniques from computational creativity may help advance assessment towards comprehensively benchmarking generative capacity and artistic exploration.