softmatch

**SoftMatch** is a **semi-supervised learning algorithm that replaces the hard confidence threshold with a soft, continuous weighting function** — assigning a sample weight between 0 and 1 based on confidence, rather than the binary keep/discard decision used in FixMatch. **How Does SoftMatch Work?** - **Weight Function**: $w(x) = exp(- ext{confidence\_deviation}^2 / 2sigma^2)$ (Gaussian weighting). - **No Threshold**: Instead of $mathbb{1}[max(p) > au]$ (hard), use a smooth weight $w(x) in [0, 1]$. - **Truncation**: Optionally truncate weights below a minimum to completely ignore very uncertain samples. - **Paper**: Chen et al. (2023). **Why It Matters** - **Soft Transition**: No abrupt cutoff at $ au$ — samples near the threshold contribute partially. - **More Data**: Moderate-confidence samples contribute to learning instead of being discarded entirely. - **Stability**: Smoother loss landscape -> more stable training dynamics. **SoftMatch** is **the gentle version of FixMatch** — using smooth weights instead of hard thresholds to extract value from all confidence levels.