freematch

**FreeMatch** is a **semi-supervised learning algorithm that uses a self-adaptive global threshold and class-specific thresholds** — automatically adjusting confidence thresholds based on the model's learning status without any fixed hyperparameter for the threshold. **How Does FreeMatch Work?** - **Self-Adaptive Threshold (SAT)**: $ au_t = lambda cdot au_{t-1} + (1-lambda) cdot frac{1}{B}sum_b max(p_b)$ (EMA of model confidence). - **Class-Fairness**: Per-class threshold adjustment based on class-specific confidence statistics. - **No Fixed $ au$**: Unlike FixMatch's fixed $ au = 0.95$, FreeMatch's threshold adapts to the model's current state. - **Paper**: Wang et al. (2023). **Why It Matters** - **Hyperparameter-Free**: Removes the need to tune the critical confidence threshold hyperparameter. - **Adaptive**: Early in training (low confidence), threshold is low. Late in training (high confidence), threshold is high. - **Robust**: Works well across different datasets and label amounts without threshold tuning. **FreeMatch** is **FixMatch that tunes itself** — automatically adapting the confidence threshold based on model's evolving capability.