Regularization
Self-Cure Network
Introduced by Wang et al. in Suppressing Uncertainties for Large-Scale Facial Expression RecognitionSelf-Cure Network, or SCN, is a method for suppressing uncertainties for large-scale facial expression recognition, prventing deep networks from overfitting uncertain facial images. Specifically, SCN suppresses the uncertainty from two different aspects: 1) a self-attention mechanism over mini-batch to weight each training sample with a ranking regularization, and 2) a careful relabeling mechanism to modify the labels of these samples in the lowest-ranked group.
Source: Suppressing Uncertainties for Large-Scale Facial Expression Recognition
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Lesion Segmentation | 1 | 12.50% |
| Specificity | 1 | 12.50% |
| Computational Efficiency | 1 | 12.50% |
| Management | 1 | 12.50% |
| Incremental Learning | 1 | 12.50% |
| Gait Recognition | 1 | 12.50% |
| Time Series Analysis | 1 | 12.50% |
| Facial Expression Recognition (FER) | 1 | 12.50% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
