Interpretability
Monte Carlo Dropout
Introduced by Gal et al. in Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Uncertainty Quantification | 46 | 12.30% |
| Prediction | 17 | 4.55% |
| Active Learning | 15 | 4.01% |
| Decision Making | 13 | 3.48% |
| Semantic Segmentation | 12 | 3.21% |
| Classification | 11 | 2.94% |
| Deep Learning | 11 | 2.94% |
| Image Segmentation | 8 | 2.14% |
| Image Classification | 7 | 1.87% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
