MFR

2000년 도입 · 논문 19편에서 사용

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.

출처: Learning Meta Face Recognition in Unseen Domains

소개 논문: Learning Meta Face Recognition in Unseen Domains