Selftraining
Self-training is a semi-supervised learning approach in which a model trained on a small labeled dataset is used to assign labels to unlabeled data, and the model is retrained on the combined labeled and pseudo-labeled data in an iterative loop.
The typical workflow is: train an initial classifier on the labeled data, apply it to the unlabeled
Self-training is related to pseudo-labeling and is a simple baseline for semi-supervised learning. It differs from
Applications span natural language processing, computer vision, speech recognition, and other domains where labeled data is
Theoretical results suggest self-training can be consistent under certain assumptions, such as low-density separation and the