flexmatch

**FlexMatch** is a **semi-supervised learning algorithm that extends FixMatch with class-specific flexible confidence thresholds** — allowing easy-to-learn classes to have higher thresholds and hard classes to have lower thresholds, improving learning fairness across classes. **How Does FlexMatch Work?** - **Curriculum**: Start with a lower threshold and increase it as the model improves. - **Per-Class**: Each class has its own dynamic threshold based on its learning status. - **Learning Status**: Track how well the model predicts each class on unlabeled data. - **Threshold**: Classes that the model already handles well get higher thresholds. Struggling classes get lower thresholds. - **Paper**: Zhang et al. (2021). **Why It Matters** - **Class Fairness**: FixMatch's fixed threshold causes the model to ignore hard classes early in training — FlexMatch fixes this. - **Curriculum Learning**: The adaptive threshold naturally creates a curriculum from easy to hard classes. - **SOTA**: Outperforms FixMatch significantly, especially with very few labels per class. **FlexMatch** is **FixMatch with class-adaptive confidence** — ensuring every class gets a fair chance to contribute pseudo-labels during training.