Semi-Supervised Learning

2 articles

Artificial Intelligence 09 Sep 2026 11 min read

Train with Unlabeled Data Using Mean Teacher

Labeled examples are often the expensive part of an AI system. You may have millions of inputs but only a small subset with trustworthy labels. Training only on the labeled subset ignores information in the rest of the data, while assigning guessed labels too aggressively can teach the model its own mistakes. Mean Teacher is a semi-supervised learning method for this situation. It trains a normal model, called the student, while maintaining a second model, called the teacher, whose parameters are an exponential moving average of the student’s parameters. The student learns from real labels when they exist and is also encouraged to make predictions that agree with the teacher on unlabeled inputs.

Artificial Intelligence Machine Learning

Artificial Intelligence 04 Sep 2026 11 min read

Mean Teacher for Semi-Supervised Learning with Unlabeled Data

Many machine learning projects have far more raw examples than labeled ones. A team may have millions of images, audio clips, or sensor readings, but only a small subset has been reviewed by people. Standard supervised training ignores the unlabeled remainder because it has no target labels to compare with the model’s predictions. Mean Teacher provides a way to use those unlabeled examples without pretending that their unknown labels are known. It trains a student model to make predictions that stay consistent with a more slowly changing teacher model. The teacher is not a separately trained expert: its parameters are an exponential moving average of the student’s parameters.

Artificial Intelligence Machine Learning