Image Data Augmentation
Cutout
Introduced by DeVries et al. in Improved Regularization of Convolutional Neural Networks with CutoutCutout is an image augmentation and regularization technique that randomly masks out square regions of input during training. and can be used to improve the robustness and overall performance of convolutional neural networks. The main motivation for cutout comes from the problem of object occlusion, which is commonly encountered in many computer vision tasks, such as object recognition, tracking, or human pose estimation. By generating new images which simulate occluded examples, we not only better prepare the model for encounters with occlusions in the real world, but the model also learns to take more of the image context into consideration when making decisions
Source: Improved Regularization of Convolutional Neural Networks with Cutout
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 22 | 20.95% |
| Object Detection | 7 | 6.67% |
| General Classification | 6 | 5.71% |
| Image Augmentation | 4 | 3.81% |
| Semantic Segmentation | 3 | 2.86% |
| Reinforcement Learning (RL) | 3 | 2.86% |
| Retrieval | 3 | 2.86% |
| Domain Generalization | 3 | 2.86% |
| Sentence | 2 | 1.90% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
