Pseudo-labeling is when:

1. A machine learning model is trained on a labeled training set.
2. The model is used to compute predicted labels against unlabeled data.
3. The model is retrained from a new dataset that adds the data with predicted labels to the training set.

Pseudo-labeling can sometimes be very effective in improving a machine learning model’s accuracy. The underlying theory is that pseudo-labeling can make it easier for a classification model to learn more precise boundaries between classes. However, in order for pseudo-labeling to work, the original training set must be large enough–and representative of all classes–for the model’s predicted labels to be reasonably accurate.

However, if the training set is already very large relative to the number of parameters in the model, then pseudo-labeling may be unnecessary.