6 Evaluation and Metrics for Generative Models

This chapter covers

Qualitative and quantitative evaluation methods, including visual inspection, user studies, and automated metrics like Inception Score and Fréchet Inception Distance
Model-specific evaluation techniques for VAEs, GANs, and Diffusion Models, addressing their unique characteristics and potential failure modes
Task-specific evaluation metrics and their application in real-world scenarios such as medical image synthesis and urban planning
Challenges and limitations in current evaluation practices, including issues of bias, computational complexity, and the lack of ground truth in generative tasks

This chapter provides a comprehensive survey of evaluation techniques and metrics for Generative AI models in computer vision. We will outline key approaches, from established methods to cutting-edge methodologies. For each technique, we will discuss its strengths, limitations, and typical use cases. By the end of this chapter, you will have a clear overview of the evaluation landscape, enabling you to select appropriate metrics for various generative model scenarios in research and practical applications.

6.1 Introduction to Model Evaluation in Generative AI

6.1.1 Importance of Evaluation in Generative Models

6.1.2 Challenges In Evaluating Generative Models For Image Synthesis

6.1.3 Overview Of Evaluation Approaches

6.2 Qualitative Evaluation Methods

6.2.1 Visual Inspection

6.2.2 User Studies

6.2.3 Summary of Qualitative Evaluation Methods

6.3 Quantitative Evaluation Metrics

6.3.1 Inception Score (IS)

6.3.2 Fréchet Inception Distance (FID)

6.3.3 Kernel Inception Distance (KID)

6.3.4 Precision and Recall for Distributions

6.3.5 Summary of Quantitative Evaluation Metrics

6.4 Model-Specific Evaluation Techniques

6.4.1 VAE-specific Metrics

6.4.2 GAN-specific Metrics

6.4.3 Diffusion Model-specific Metrics

6.5 Task-Specific Evaluation Metrics

6.5.1 Case Study 1: Super-Resolution for Remote Sensing Imagery

6.5.2 Case Study 2: Medical Image Synthesis for Data Augmentation

6.6 Challenges and Limitations in Evaluation

6.6.1 Bias in Evaluation Metrics

6.6.2 Computational Complexity and Scalability

6.6.3 Lack of Ground Truth in Generative Tasks

6.6.4 Domain Specificity and Generalization

6.7 Conclusion

6.8 Summary