fixmatch

**FixMatch** is a **semi-supervised learning algorithm that combines pseudo-labeling with consistency regularization** — using weak augmentation to generate confident pseudo-labels and strong augmentation to create challenging training targets, achieving near-supervised performance with very few labels. **How Does FixMatch Work?** - **Weak Augmentation**: Apply weak augmentation (flip, crop) to unlabeled data -> generate prediction. - **Pseudo-Label**: If $max(p_{weak}) > au$ (typically $ au = 0.95$), use $argmax(p_{weak})$ as a hard pseudo-label. - **Strong Augmentation**: Apply strong augmentation (RandAugment, CTAugment) to the same unlabeled image. - **Loss**: Cross-entropy between the pseudo-label and the model's prediction on the strongly augmented version. - **Paper**: Sohn et al. (2020). **Why It Matters** - **Simplicity**: Two simple ideas (confidence pseudo-labeling + weak/strong augmentation) combined elegantly. - **Few Labels**: 250 labels on CIFAR-10 → 94.9% accuracy (vs. 95.0% supervised with 50K labels). - **Standard**: Became the baseline for semi-supervised learning research. **FixMatch** is **the elegant union of pseudo-labeling and consistency** — using weak views for labels and strong views for training in a remarkably effective combination.