Self Training

Self training is a semi-supervised learning technique in machine learning where a model is trained on a small labeled dataset and then used to generate pseudo-labels for a larger unlabeled dataset, which are then used to retrain the model. It leverages both labeled and unlabeled data to improve model performance, particularly in scenarios where labeled data is scarce or expensive to obtain. This iterative process helps the model generalize better by learning from its own predictions on unlabeled examples.

Also known as: Self-Training, Self-Learning, Pseudo-Labeling, Semi-Supervised Self-Training, ST

Why learn Self Training?

Developers should learn self training when working on machine learning projects with limited labeled data, such as in natural language processing, computer vision, or any domain where annotation is costly. It is especially useful for tasks like text classification, image recognition, or anomaly detection, as it can significantly boost accuracy without requiring extensive manual labeling. By mastering self training, developers can build more robust models efficiently, making it a valuable skill for data scientists and ML engineers.

Learning Resources

• docs: Semi-Supervised Learning with Self-Training
• tutorial: Self-Training Tutorial on Towards Data Science
• course: Self-Training in Machine Learning - Coursera Course
• video: Self-Training Explained - YouTube Video