FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence

Understand

Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.

• In this paper, we demonstrate the power of a simple combination of two common SSL methods: consistency regularization and pseudo-labeling.
• Our algorithm, FixMatch, first generates pseudo-labels using the model's predictions on weakly-augmented unlabeled images.
• For a given image, the pseudo-label is only retained if the model produces a high-confidence prediction.