Face Recognition Models
Meta Face Recognition
Introduced by Guo et al. in Learning Meta Face Recognition in Unseen DomainsMeta Face Recognition (MFR) is a meta-learning face recognition method. MFR synthesizes the source/target domain shift with a meta-optimization objective, which requires the model to learn effective representations not only on synthesized source domains but also on synthesized target domains. Specifically, domain-shift batches are built through a domain-level sampling strategy and back-propagated gradients/meta-gradients are obtained on synthesized source/target domains by optimizing multi-domain distributions. The gradients and meta-gradients are further combined to update the model to improve generalization.
Source: Learning Meta Face Recognition in Unseen Domains
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Face Recognition | 8 | 50.00% |
| Change Detection | 1 | 6.25% |
| Change Point Detection | 1 | 6.25% |
| TAR | 1 | 6.25% |
| Translation | 1 | 6.25% |
| Optical Flow Estimation | 1 | 6.25% |
| Computational Efficiency | 1 | 6.25% |
| Specificity | 1 | 6.25% |
| Meta-Learning | 1 | 6.25% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
