cutmix

**CutMix** is a **data augmentation technique that cuts a rectangular region from one image and pastes it onto another** — mixing the labels proportionally to the area of the cut region, combining the benefits of Cutout (occlusion robustness) and Mixup (label smoothing). **How Does CutMix Work?** - **Sample $lambda$**: $lambda sim ext{Beta}(alpha, alpha)$. - **Cut Region**: Random box with area ratio $1 - lambda$ of the total image. - **Paste**: Replace the cut region in image $A$ with the corresponding region from image $B$. - **Labels**: $ ilde{y} = lambda y_A + (1-lambda) y_B$ (proportional to visible area). - **Paper**: Yun et al. (2019). **Why It Matters** - **Best of Both**: Unlike Mixup (blurry blends) or Cutout (wasted pixels), CutMix uses all pixel information. - **Localization**: Forces the model to learn from local regions, improving weakly-supervised localization. - **SOTA**: Widely adopted in modern ImageNet training recipes alongside Mixup and RandAugment. **CutMix** is **a surgical transplant between images** — cutting and pasting regions to create informative training samples that use every pixel.