How to evaluate image generation

Evaluating generative AI for vision (GenAI vision) is an evolving field, but here are some key approaches to consider:

Human Evaluation:

Subjective Assessment: Since "good" for a generated image can be subjective, human evaluation is crucial. Recruit users to rate the outputs on factors like:
Photorealism: How realistic and detailed does the image appear?
Relevance: Does the image accurately reflect the prompt or concept?
Style: Does the image adhere to the desired artistic style (e.g., impressionistic, photorealistic)?
Creativity: Does the image go beyond a basic representation and showcase originality?
Diversity: Does the model generate a variety of outputs for the same prompt, avoiding monotony?

Platforms for Human Evaluation:

Tools like Adobe GenLens or Replicate Zoo can streamline the human evaluation process by providing interfaces for collecting user ratings on generated images.

Automatic Metrics:

Limited Effectiveness: While helpful in other domains, traditional metrics like Mean Squared Error (MSE) or Structural Similarity Index (SSIM) may not fully capture the quality of generated images. They focus on pixel-level differences which might not reflect the high-level content or style.

Emerging Techniques:

Frechet Inception Distance (FID): This metric attempts to assess the quality of generated images by measuring the distance between the distribution of features extracted from real images and the generated ones.