Random Erasing

Random Erasing in ViT is a data augmentation technique that randomly masks rectangular patches in input images during Vision Transformer training to improve robustness and reduce overfitting.

## What Is Random Erasing?

• Method: Replace random image regions with random values or mean pixel
• Parameters: Probability, area ratio (0.02-0.4), aspect ratio
• Effect: Forces model to learn from partial information
• Origin: Zhong et al. 2017, widely adopted in ViT training

## Why Random Erasing Matters

ViTs can overfit to specific image regions. Random erasing encourages attention to diverse features and improves generalization.

Random Erasing in ViT Recipe:

transforms.Compose([
    transforms.RandomResizedCrop(224),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
    RandomErasing(
        probability=0.25,
        sl=0.02, sh=0.4,  # area ratio
        r1=0.3,           # aspect ratio min
    ),
])

Typical improvement: +0.5-1.5% top-1 accuracy on ImageNet.