Zero-Shot Classification System

Build a zero-shot classification system without task-specific training.

Classification needs:
- Input type: [TEXT/IMAGE/BOTH]
- Class descriptions: [HOW CLASSES ARE DEFINED]
- Number of classes: [FIXED/DYNAMIC]
- Update frequency: [HOW OFTEN CLASSES CHANGE]

Zero-shot components:
1. Text classification:
   - Entailment-based (NLI)
   - Prompt-based with LLMs
   - Semantic similarity
2. Image classification:
   - CLIP-based
   - Vision-language models
   - Prompt engineering for vision
3. Label engineering:
   - Class description design
   - Template optimization
   - Synonym expansion
4. Confidence calibration:
   - Score normalization
   - Threshold selection
   - Uncertainty estimation
5. Evaluation:
   - Zero-shot accuracy
   - Comparison with few-shot
   - Class-wise analysis
6. Optimization:
   - Ensemble methods
   - Label refinement
   - Adaptive prompting
7. Production:
   - Dynamic class updates
   - Efficient inference
   - Feedback integration

No labeled data required for new classes.