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name: latent_pair_protocol description: Validate DCL latent-paired source/target feature batches and separate real-target negatives for few-shot GAN adaptation recovery.

Latent Pair Protocol

Use this skill when a recovery experiment must construct or validate a DCL-style batch. It is appropriate for reduced feature-level experiments and for checking that source and target generated samples share latent identifiers. Do not use it to compute the contrastive loss or evaluation metric.

Inputs

• Source generated feature records with latent_id and numeric features.
• Target generated feature records with matching latent_id and numeric features.
• Few real target exemplar feature records.

Outputs

• A JSON-compatible batch with ordered latent pairs.
• Separate generated negatives and real-target negatives.
• Validation errors for missing or duplicated latent ids.

Workflow

1. Normalize feature records into lists of floats.
2. Require exactly one source and one target record per latent id.
3. Build positives from same-latent source/target records.
4. Keep real-target exemplars separate so later DCL loss code can apply anti-collapse negatives.

Validation

Run python tests/test_latent_pair_protocol.py from this skill directory.

Limitations

This skill validates feature-level protocol semantics only; it does not implement StyleGAN, discriminator features, FID, or LPIPS.

Refinement Note

Recovery now includes an edge-case check that mismatched source/target latent ids are rejected before loss computation.