cutout

**Cutout** is a **data augmentation technique that randomly masks (zeroes out) a square region of the input image** — forcing the model to learn from partial information and preventing over-reliance on any single region of the image. **How Does Cutout Work?** - **Random Position**: Select a random center position $(x, y)$ in the image. - **Mask**: Zero out a square patch of size $L imes L$ centered at $(x, y)$. - **Boundary**: The mask can extend beyond the image boundary (partial occlusion is still applied). - **Typical Size**: $L = 16$ for CIFAR-10 (32×32 images), scaling up for larger images. - **Paper**: DeVries & Taylor (2017). **Why It Matters** - **Robustness**: Teaches the model to classify using any visible part of the object, not just the most discriminative region. - **Occlusion Handling**: Simulates real-world partial occlusion scenarios. - **Simple & Effective**: Consistently improves accuracy by 0.5-1.0% on CIFAR and ImageNet with no tuning. **Cutout** is **learning with missing information** — randomly hiding parts of the image to create a more robust feature extractor.