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%.
Table of contents
Teaching Your Model to Learn from ItselfHow Does it Work?The Echo Chamber Effect: Can Pseudo-Labeling Even Work?Case Study: MNIST DatasetKey Findings and Lessons LearnedLinks1 Comment
Sort: