Methods › Computer Vision › Face Recognition Models › MFR

Meta Face Recognition

MFR

19 papers tagged archive 2025-07-28

Introduced by Jianzhu Guo et al. in Learning Meta Face Recognition in Unseen Domains

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSource

Papers archive 2025-07-28

19 shown of 19, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 25 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Face Recognition10
Anomaly Detection1
Bayesian Inference1
Change Detection1
Change Point Detection1
Code Generation1
Computational Efficiency1
Data Augmentation1
Dataset Generation1
GSM8K1
Image Super-Resolution1
Math1
Meta-Learning1
Missing Values1
Optical Flow Estimation1
Position1
Scheduling1
Specificity1
Super-Resolution1
Survey1

Usage over time archive 2025-07-28

Papers per year tagged with MFR: 2020 to 2025, peak 6 6 0 2020: 2 papers 2020 2021: 6 papers 2021 2022: 0 papers 2022 2023: 6 papers 2023 2024: 4 papers 2024 2025: 1 paper 2025
Papers per year the archive tags with this method, by the paper's archive date (19 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Face Recognition Models

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