Researchers and developers often struggle with the problem of having too little labeled training data. One approach to overcome this limitation is semi-supervised learning, which uses both labeled and unlabeled examples to improve model performance. Semi-supervised methods can be divided into four main categories: pseudo-labeling, consistency regularization, entropy minimization, and multi-instance learning. These techniques aim to leverage the available labeled data more efficiently and make better use of the unlabeled data. --- Why it matters: Semi-supervised learning matters because it enables developers to build accurate models with limited labeled training data, which is a common challenge in many AI applications. Source: https://lilianweng.github.io/posts/2021-12-05-semi-supervised/