Siamese Neural Network Specialist — Scaffold Workflow

Description: Generate new artifact from scratch

When to Use

Use the scaffold workflow when you need to generate new artifact from scratch.

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 scaffold context for Siamese Neural Network Specialist
2. Execute — Perform the scaffold 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