Editing Semi Supervised (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Semi-supervised learning works by exploiting the '''structure of the unlabeled data distribution''' to constrain the label function. The key assumptions:

'''Smoothness''': Nearby points → similar labels. If two images of dogs are close in feature space, they should both be labeled "dog."

'''Cluster''': Classes form clusters. The decision boundary should pass through low-density regions between clusters, not through high-density regions.

'''Manifold''': Data lies on lower-dimensional manifolds. Using unlabeled data to learn the manifold structure helps place decision boundaries correctly.

'''Self-training process''':
# Train on labeled data.
# Predict labels for unlabeled data.
# Add high-confidence predictions to training set.
# Retrain.
# Repeat. Risk: confident errors propagate (confirmation bias). Mitigated by strict confidence thresholds.

'''FixMatch''': The state-of-the-art simple baseline. For each unlabeled image:
# Apply weak augmentation (horizontal flip, crop).
# If prediction confidence > 0.95, use as pseudo-label.
# Apply strong augmentation (RandAugment).
# Train student to predict the pseudo-label on the strongly augmented view. This enforces consistency across augmentation strengths while only training on confident pseudo-labels.

'''When does semi-supervised help most?''' When labeled data is very scarce (<1000 examples) and unlabeled data shares the same distribution as labeled data. When distributions differ (domain shift between labeled and unlabeled), semi-supervised can hurt — a form of negative transfer.
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