MFR
Meta Face Recognition
**Meta 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.
Learning Meta Face Recognition in Unseen Domains
https://paperswithcode.com/paper/learning-meta-face-recognition-in-unseen
Learning Meta Face Recognition in Unseen Domains