curriculum pseudo-labeling

**Curriculum Pseudo-Labeling** is a **semi-supervised learning strategy that progressively introduces pseudo-labeled samples in order of difficulty** — starting with the most confident (easiest) predictions and gradually including less certain samples as the model improves. **How Does It Work?** - **Easy First**: Initially, only use pseudo-labels with very high confidence. - **Progressive Relaxation**: As training progresses, lower the confidence threshold to include harder samples. - **Schedule**: Threshold decreases linearly, cosine, or based on model performance metrics. - **Self-Paced**: The curriculum naturally adapts to the model's learning stage. **Why It Matters** - **Error Prevention**: High-confidence-first avoids early training on incorrect pseudo-labels. - **Curriculum Learning**: Follows the proven curriculum learning paradigm (easy to hard). - **Used In**: FlexMatch, Dash, and other modern semi-supervised methods incorporate curriculum ideas. **Curriculum Pseudo-Labeling** is **learning from the easiest examples first** — gradually building confidence before tackling harder unlabeled samples.