How to evaluate image generation
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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. |
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