sharpening

**Sharpening** is a **technique that reduces the entropy of a probability distribution by raising it to a power (lowering the temperature)** — making confident predictions more confident and encouraging the model to commit to a single class rather than spreading probability mass. **How Does Sharpening Work?** - **Temperature Scaling**: $ ext{Sharpen}(p, T)_i = p_i^{1/T} / sum_j p_j^{1/T}$ where $T < 1$ sharpens. - **Low $T$**: Approaches one-hot (argmax). **High $T$**: Approaches uniform. **$T = 1$**: No change. - **Typical $T$**: 0.5 in MixMatch, 0.3-0.7 in general semi-supervised learning. **Why It Matters** - **Entropy Minimization**: Encourages the model to make confident predictions on unlabeled data (cluster assumption). - **MixMatch**: Sharpening is a core component of MixMatch, applied to averaged pseudo-label predictions. - **Soft Labels**: Unlike hard pseudo-labels, sharpened soft labels preserve uncertainty ranking among classes. **Sharpening** is **turning up the contrast on predictions** — making the model's soft predictions crisper and more decisive.