temperature sharpening

**Temperature Sharpening** is the **specific application of temperature scaling to sharpen (reduce entropy of) prediction distributions** — a key component in semi-supervised learning and knowledge distillation, where the temperature parameter $T$ controls the softness or hardness of the output distribution. **Temperature Effects** - **$T ightarrow 0$**: Distribution becomes one-hot (hard label). Maximum confidence. - **$T = 1$**: Standard softmax. No modification. - **$T > 1$**: Distribution becomes softer/more uniform. Used in knowledge distillation. - **$T < 1$**: Distribution becomes sharper. Used in semi-supervised learning for pseudo-labels. **Why It Matters** - **Two Use Cases**: $T > 1$ for distillation (soft targets), $T < 1$ for semi-supervised (sharpen pseudo-labels). - **Confidence Control**: Provides a continuous knob between soft uncertainty ($T$ high) and hard commitment ($T$ low). - **Universal**: Used in MixMatch, FixMatch, knowledge distillation, and contrastive learning (InfoNCE temperature). **Temperature Sharpening** is **the confidence knob** — a single parameter that controls how decisive or uncertain the model's predictions appear.