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name: diversity_correspondence_metrics description: Compute diversity and correspondence diagnostics for few-shot image generation recovery experiments.

Diversity and Correspondence Metrics

Use this skill when evaluating a few-shot generator adaptation run where overfitting and correspondence preservation matter. It provides deterministic vector analogues of the paper's intra-cluster LPIPS diversity and source-target correspondence analysis.

Inputs

• Generated feature vectors.
• Target anchor vectors for nearest-anchor cluster assignment.
• Optional source and adapted vectors for pairwise similarity-correlation analysis.

Outputs

• Average intra-cluster pairwise distance.
• Cluster sizes and assignments.
• Pairwise similarity correlation between source and adapted features.
• A concise metric summary for analysis artifacts.

Workflow

1. Assign every generated vector to the nearest target anchor.
2. Compute all pairwise Euclidean distances inside each non-singleton cluster.
3. Average cluster distances, treating singleton clusters as zero-diversity evidence.
4. If source and adapted features are present, compute correlation between their pairwise cosine similarity lists.
5. Report numeric metrics and preserve enough assignments for debugging.

Validation

Run python tests/test_metrics.py or validate this skill tree with validate_skill_tree.py --run-tests.

Limitations

This skill uses generic vector distances in reduced recovery. Full paper-level evaluation should replace them with LPIPS/FID when the image stack and datasets are available.