Teaching Your Model to Learn from Itself

In machine learning, labeling data can be expensive and time-consuming. Pseudo-labeling offers a solution by using confident predictions on unlabeled data to iteratively improve model accuracy. In a case study using the MNIST dataset, applying iterative, confidence-based pseudo-labeling increased model accuracy from 90% to 95%. Key strategies include maintaining rigorous thresholds, continuous performance evaluation, and incorporating human feedback for low-confidence data.