Image Data Augmentation
Random Erasing
Introduced by Zhong et al. in Random Erasing Data AugmentationRandom Erasing is a data augmentation method for training the convolutional neural network (CNN), which randomly selects a rectangle region in an image and erases its pixels with random values. In this process, training images with various levels of occlusion are generated, which reduces the risk of over-fitting and makes the model robust to occlusion. Random Erasing is parameter learning free, easy to implement, and can be integrated with most of the CNN-based recognition models. Random Erasing is complementary to commonly used data augmentation techniques such as random cropping and flipping, and can be implemented in various vision tasks, such as image classification, object detection, semantic segmentation.
In the Albumentations library, there is a generalization of RandomErasing called CoarseDropout, which allows masking an arbitrary number of regions of rectangular shape.
It could be applied to images, segmentation masks, and key points.
Documentation for CoarseDropout
Source: Random Erasing Data Augmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 5 | 14.29% |
| Person Re-Identification | 3 | 8.57% |
| Object Detection | 2 | 5.71% |
| General Classification | 2 | 5.71% |
| breast density classification | 1 | 2.86% |
| Instance Segmentation | 1 | 2.86% |
| Semantic Segmentation | 1 | 2.86% |
| Fine-Grained Image Classification | 1 | 2.86% |
| Pose Estimation | 1 | 2.86% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
