distribution alignment

**Distribution Alignment** is a **technique in semi-supervised learning that adjusts pseudo-label distributions to match the true class distribution** — preventing the model from being biased toward classes it finds easy to predict and ensuring balanced utilization of pseudo-labels. **How Does Distribution Alignment Work?** - **Estimate**: Track the running average of pseudo-label class distribution $hat{p}(y)$. - **Target**: The expected class distribution $p(y)$ (uniform for balanced datasets, or estimated from labeled data). - **Align**: Adjust predictions: $ ilde{p}(y|x) = p(y|x) cdot p(y) / hat{p}(y)$ (reweight to match target distribution). - **Normalize**: Renormalize the adjusted distribution to sum to 1. **Why It Matters** - **Class Balance**: Prevents positive feedback loops where easy classes dominate pseudo-labels. - **Long-Tail**: Critical for class-imbalanced datasets where some classes are rarely predicted. - **MixMatch/ReMixMatch**: Distribution alignment is a key component of these popular semi-supervised methods. **Distribution Alignment** is **class balance enforcement for pseudo-labels** — correcting the model's class biases to ensure all classes are fairly represented.