Mehta, A. (2019, June 29). Retrieved from [www.digitalvidya.com/blog/semi-supervised-learning/](https://www.digitalvidya.com/blog/semi-supervised-learning/) 

An Ultimate Guide to Understanding Semi-Supervised Learning

This image shows the pseudo labeling process:
1. Labeled data is used to train a model
2. Unlabeled data is then labeled by the trained model (pseudo labeled data)
3. Both the pseudo labeled data and labeled data are then combined into the full dataset
4. The full dataset is then used to retrain the model


University of Michigan - Ann Arbor

Johns Hopkins University

Pseudo labeling is a process used in semi-supervised learning, where it combines both supervised and unsupervised techniques in order to train a model. The algorithm is as follows:
1. You use the portion of your data set that is already labeled as the training set to train your model (supervised learning)
2. Once the model is giving you good results, you then use that model with your unlabeled data (unsupervised learning)
    - The model will predict the outcomes of the unlabeled data. We do not know if these are correct, but we have accurate labels after training it with our labeled data
3. We then combine both the original training set with the unlabeled data that was just labeled, training our model on the full data set
Pseudo labeling helps improve the model as well as reduce the error.

Learn Before

Related

Learn After