INFO
Semi-supervised deep learning combines supervised and unsupervised techniques to leverage both labeled and unlabeled data, enabling scalable learning when labeled data is scarce.
Process
- Initial training on labeled data
- Iterative refinement using unlabeled data
- Common techniques:
- Applied in domains such as:
- Healthcare diagnostics
- Natural language understanding
- Image classification and segmentation
Advantages
- Reduces labeling cost and manual effort
- Improves generalization and model robustness
- Scales effectively with growing unlabeled datasets
- Balances accuracy and training efficiency
Disadvantages
- Sensitive to quality of pseudo-labels
- Requires careful tuning of regularization strategies
- May propagate errors from mislabeled or noisy data
- Evaluation can be complex due to mixed supervision