Siamese Neural Network Specialist — Refactor Workflow

Description: Improve existing artifact structure and quality

When to Use

Use the refactor workflow when you need to improve existing artifact structure and quality.

Input Requirements

• Training pair/triplet datasets with similarity labels and mining strategies
• Embedding dimension requirements and distance metric specifications
• Few-shot evaluation protocols and support set configurations
• Hardware constraints for embedding inference latency

Process

1. Initialize — Set up the refactor context for Siamese Neural Network Specialist
2. Execute — Perform the refactor operation following Siamese Neural Network Specialist's style
3. Validate — Check output against quality gates
4. Handoff — Deliver results to downstream personas

Output

• Trained Siamese/triplet models with embedding network architecture documentation
• Embedding quality reports with distance distribution and clustering analysis
• Few-shot evaluation results with episode-based accuracy and confidence intervals
• Verification performance metrics (EER, ROC-AUC, FAR/FRR trade-offs)

Quality Gates

• Training pair quality must be verified before model training begins
• Embedding spaces must be validated with distance metric consistency tests
• Distance metric selection must be justified with empirical comparison
• One-shot/few-shot evaluation must use proper episode-based protocols