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---
name: intra_lpips_proxy_evaluation
description: Compute a deterministic intra-cluster diversity proxy mirroring intra-LPIPS evaluation for reduced few-shot generation recovery.
---
# Intra-LPIPS Proxy Evaluation
Use this skill when LPIPS networks are unavailable but recovery needs to preserve the paper's intra-cluster diversity evaluation structure. It computes nearest-target clusters and average within-cluster Euclidean distances over features. Do not report it as real LPIPS.
## Inputs
- Generated feature samples.
- Few target exemplar features.
## Outputs
- Average intra-cluster distance proxy.
- Cluster assignments and pair counts.
## Workflow
1. Assign each generated feature to its nearest target exemplar.
2. For each cluster, compute all pairwise generated-feature distances.
3. Average distances across non-singleton clusters.
4. Return zero for exact memorization or singleton-only clusters.
## Validation
Run `python tests/test_intra_lpips_proxy_evaluation.py` from this skill directory.
## Limitations
This metric is only a feature-distance proxy for intra-LPIPS; it is useful for mechanism checks but not comparable to paper LPIPS values.
## Refinement Note
Include a memorization ablation: exact copies of target exemplars should have lower or zero intra-cluster diversity than adapted generated features.
