Core Concepts

• - Uses both labeled and unlabeled data in one training loop.
• - Pseudo-labeling extends supervision when labels are limited.
• - Consistency regularization improves representation stability.
• - Confidence filtering controls error propagation.

Professional Workflow

1. 1. Train seed model on small labeled subset.
2. 2. Generate pseudo-labels on unlabeled samples.
3. 3. Keep only high-confidence pseudo-labels for retraining.
4. 4. Iterate and track validation stability each round.