Optimization
Sharpness-Aware Minimization
Introduced by Foret et al. in Sharpness-Aware Minimization for Efficiently Improving GeneralizationSharpness-Aware Minimization, or SAM, is a procedure that improves model generalization by simultaneously minimizing loss value and loss sharpness. SAM functions by seeking parameters that lie in neighborhoods having uniformly low loss value (rather than parameters that only themselves have low loss value).
Source: Sharpness-Aware Minimization for Efficiently Improving Generalization
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Image Classification | 13 | 18.06% |
| Domain Generalization | 4 | 5.56% |
| Adversarial Robustness | 3 | 4.17% |
| Language Modelling | 2 | 2.78% |
| Federated Learning | 2 | 2.78% |
| Node Classification | 2 | 2.78% |
| Long-tail Learning | 2 | 2.78% |
| Model Compression | 2 | 2.78% |
| Quantization | 2 | 2.78% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
