Editing Semi Supervised (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Semi-supervised learning''' — Learning using a small labeled dataset and a large unlabeled dataset simultaneously.
* '''Pseudo-labeling''' — Using a model's predictions on unlabeled data as provisional labels, then retraining on those labels.
* '''Consistency regularization''' — Enforcing that model predictions remain consistent under perturbations of unlabeled inputs.
* '''Mean Teacher''' — A semi-supervised method where a student model is trained, and the teacher model is an exponential moving average of student weights; teacher provides pseudo-labels.
* '''FixMatch''' — A state-of-the-art semi-supervised image classification method using confidence thresholding and weak/strong augmentation consistency.
* '''MixMatch''' — A holistic semi-supervised approach combining pseudo-labeling, consistency regularization, and MixUp data augmentation.
* '''Self-training''' — Train on labeled data, predict labels for unlabeled data, retrain on the combination; repeat iteratively.
* '''Co-training''' — Train two models on different feature views; each provides pseudo-labels for the other.
* '''Graph-based methods''' — Propagate labels through a graph where edges represent similarity between examples (label propagation).
* '''Label propagation''' — Semi-supervised algorithm that spreads labels from labeled to unlabeled examples through a similarity graph.
* '''Manifold assumption''' — The assumption that data lies on a low-dimensional manifold; points on the same manifold should have the same label.
* '''Smoothness assumption''' — If two points are close in input space, they should have similar labels.
* '''Cluster assumption''' — Decision boundaries should lie in low-density regions between clusters.
* '''Confidence threshold''' — In pseudo-labeling, only use predictions where model confidence exceeds a threshold; avoids noisy pseudo-labels.
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