How can you measure the quality of generated samples?

Measuring the quality of generated samples, such as those produced by generative models like GANs or diffusion models, involves a mix of automated metrics and human evaluation. The choice of method depends on the type of data (e.g., images, text) and the specific goals of the project. Below, I’ll outline common approaches, their strengths, and limitations.

Automated Metrics for Objective Assessment Automated metrics provide a quantitative way to evaluate samples. For images, the Inception Score (IS) measures both the diversity and meaningfulness of generated images using a pre-trained image classifier. A higher score indicates that the model produces distinct and recognizable categories. Another metric, the Fréchet Inception Distance (FID), compares the statistical distribution of generated images to real ones using features extracted by a classifier. Lower FID values mean the generated data is closer to real data. For text, metrics like BLEU or ROUGE compare generated text to reference texts by measuring overlap in n-grams or semantic similarity. These metrics are efficient for large-scale evaluation but may not capture subtle flaws, such as unnatural textures in images or awkward phrasing in text.

Human Evaluation for Subjective Quality Automated metrics often miss nuances that humans notice easily. For example, a generated image might score well on FID but contain distorted facial features. Human evaluators can rate samples for realism, coherence, or aesthetic quality using surveys or pairwise comparisons (e.g., asking which of two samples looks more realistic). In text generation, humans assess fluency, relevance, and logical consistency. While this approach captures subjective quality, it’s time-consuming and expensive. To mitigate bias, evaluations should involve multiple annotators and clear guidelines. For instance, in a project generating product descriptions, evaluators might check if the text aligns with the product’s specifications and avoids factual errors.

Task-Specific Metrics and Trade-offs In some cases, domain-specific metrics are necessary. For medical imaging, accuracy in generated anatomical structures might be measured against expert annotations. In code generation, functional correctness (e.g., whether the code compiles or passes unit tests) is critical. However, no single metric works universally. Developers must balance speed, cost, and relevance: automated metrics are fast but limited, while human evaluation is thorough but impractical for iterative testing. A hybrid approach—using automated metrics during development and human evaluation for final validation—is often effective. For example, a team training a text-to-image model might monitor FID during training but conduct user studies before deployment to ensure the outputs meet real-world standards.