Semi-supervised learning is a hybrid of both supervised and unsupervised methods. ML requires large amounts of data for training. Most of the time, a directly proportional relationship is observed between the amount of data used for model training and the performance of the model.

In niche domains such as medical imagining, a large amount of image data (MRIs, x-rays, CT scans) is available. However, the time and availability of qualified radiologists to label these images is scarce. In this situation, we might end up getting only a few images labeled by radiologists.

Semi-supervised learning takes advantage of the few labeled images by building an initial model that is used to label the large amount of unlabeled data that exists in the domain. Once the large amount of labeled data is available, a supervised ML algorithm may be used to train and create a final model that is used for prediction tasks on the unseen data. The following diagram illustrates the steps involved in semi-supervised learning:

Speech analysis, protein synthesis, and web content classifications are certain areas where large amounts of unlabeled data and fewer amounts of labeled data are available. Semi-supervised learning is applied in these areas with successful results.

Generative adversarial networks (GANs), semi-supervised support vector machines (S3VMs), graph-based methods, and Markov chain methods are well-known methods among others in the semi-supervised ML area.