1. Introduction  
2. Active Learning  
2.1. Query Types  
2.2. Evaluating Informativeness  
2.3. Fine-tuning vs Retraining  
3. The Final Percent: Interactive Refinement of Model Outputs  
3.1. Interactive Refinement  
4. Practical Considerations  
4.1. Noisy Oracles  
4.2. Weakly Supervised Learning  
4.3. Multi-task Learning  
4.4. Annotation Interface  
4.5. Variable Learning Costs  
5. Future Prospective and Unanswered Questions  
6. Conclusions