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.
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**Random Erasing in ViT Recipe**:
```python
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.