Normalization
Synchronized Batch Normalization
Introduced by Zhang et al. in Context Encoding for Semantic SegmentationSynchronized Batch Normalization (SyncBN) is a type of batch normalization used for multi-GPU training. Standard batch normalization only normalizes the data within each device (GPU). SyncBN normalizes the input within the whole mini-batch.
Source: Context Encoding for Semantic Segmentation
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Object Detection | 2 | 16.67% |
| Semantic Segmentation | 2 | 16.67% |
| Image Augmentation | 1 | 8.33% |
| Road Damage Detection | 1 | 8.33% |
| 2D Object Detection | 1 | 8.33% |
| Novel Object Detection | 1 | 8.33% |
| Object Localization | 1 | 8.33% |
| Scene Parsing | 1 | 8.33% |
| Image Classification | 1 | 8.33% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
