pseudo-labeling with confidence

**Pseudo-Labeling with Confidence** is a **semi-supervised learning technique that uses the model's own high-confidence predictions on unlabeled data as training labels** — filtering predictions by a confidence threshold to ensure only reliable pseudo-labels are used. **How Does It Work?** - **Predict**: Run unlabeled data through the current model. - **Filter**: Keep only predictions where $max(p(y|x)) > au$ (confidence threshold, typically $ au = 0.95$). - **Train**: Use filtered pseudo-labeled data alongside labeled data with cross-entropy loss. - **Iterate**: Retrain or update the model, then re-predict and re-filter. **Why It Matters** - **Simplicity**: The simplest semi-supervised learning method — no architectural changes needed. - **FixMatch**: The confidence threshold is the core component of FixMatch and modern semi-supervised methods. - **Self-Training**: A form of self-training that bootstraps labeled data from model confidence. **Pseudo-Labeling** is **the model teaching itself** — using high-confidence predictions as targets to leverage the vast pool of unlabeled data.