Problem

MISSING: Knowledge distillation transfers knowledge from large teacher models to smaller student models, achieving comparable performance with much smaller size.

Missing Implementations

Standard Distillation (CRITICAL):

• Response-based: soft labels (temperature scaling)
• Feature-based: intermediate layer matching
• Relation-based: similarity between samples

Advanced Techniques (HIGH):

• Self-distillation (student = teacher architecture)
• Cross-modal distillation (different modalities)
• Online distillation (peer teaching)
• Multi-teacher distillation

Task-Specific (HIGH):

• Classification distillation
• Object detection distillation (faster R-CNN → YOLO)
• Language model distillation (BERT → DistilBERT)
• Vision transformer distillation (ViT → DeiT)

Loss Functions:

• KL divergence for soft labels
• MSE for feature matching
• Attention transfer
• Combined task loss + distillation loss

Use Cases

• Create lightweight models for deployment
• Compress GPT-3 → GPT-2 size with 95% performance
• Mobile/edge deployment
• Faster inference

Architecture

Success Criteria

• DistilBERT-like results (40% smaller, 97% performance)
• Integration with existing training loop
• Teacher-student architecture flexibility
• Benchmarks on ImageNet, GLUE