Semi-Supervised Learning

Semi-supervised learning uses a small labelled dataset alongside a much larger pool of unlabelled data to train a better model than either dataset could produce alone. The intuition is that unlabelled data reveals the geometry of the input distribution — clusters, manifolds, boundaries — while the labels tell the model which regions map to which classes. Combining the two lets the labelled examples propagate their supervision outward through the unlabelled neighbourhood.

The classical techniques are **self-training** (train on the labelled set, predict pseudo-labels on the unlabelled set, retrain on both), **co-training** (two models trained on different feature views teach each other), and **graph-based label propagation** (spread labels through a similarity graph). The modern deep-learning incarnation is **consistency regularisation** — force the model to produce the same output for two augmented views of the same unlabelled input (FixMatch, MixMatch, SimCLR-style pretraining + fine-tuning).

For APAC mid-market, semi-supervised learning is especially valuable when labelling is expensive (medical imaging, legal documents, niche quality-control scenarios) but unlabelled data is plentiful (every document your business ever processed). A typical project structure: label 500–2000 examples carefully, pretrain or self-train on 100K+ unlabelled examples, and reach quality that would have required 10× the labels under pure supervised training.

The honest caveat: the distinction between modern semi-supervised learning and **self-supervised pretraining + supervised fine-tuning** has blurred. For most real-world tasks involving text or images, using a foundation model (pretrained self-supervised on web-scale data) plus a small labelled fine-tuning set is easier, higher-quality, and more operationally boring than rolling your own semi-supervised pipeline. The techniques above still matter in regulated or isolated-data settings where a domain-specific pretrained base is not available.