Methods › Computer Vision › Face Recognition Models › CurricularFace

CurricularFace

3 papers tagged archive 2025-07-28

Introduced by Yuge Huang et al. in CurricularFace: Adaptive Curriculum Learning Loss for Deep Face Recognition

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

CurricularFace, or Adaptive Curriculum Learning, is a method for face recognition that embeds the idea of curriculum learning into the loss function to achieve a new training scheme. This training scheme mainly addresses easy samples in the early training stage and hard ones in the later stage. Specifically, CurricularFace adaptively adjusts the relative importance of easy and hard samples during different training stages.

PaperSource

Papers archive 2025-07-28

3 shown of 3, 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

7 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 Recognition2
Adversarial Attack1
Face Image Quality1
Face Image Quality Assessment1
Face Verification1
Image Quality Assessment1
Management1

Usage over time archive 2025-07-28

Papers per year tagged with CurricularFace: 2020 to 2024, peak 1 1 0 2020: 1 paper 2020 2021: 0 papers 2021 2022: 1 paper 2022 2023: 0 papers 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (3 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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