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GroupFace: Learning Latent Groups and Constructing Group-Based Representations for Face Recognition

CVPR 2020 · Label-efficient and robust learning › Recognition and long-tail · approach: Margin-based face recognition losses · 94 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add group-aware representations to ArcFaceArcFace with ResNet-100 · IJB-BTAR@FAR=1e-6: 0.3828 -> 0.4678Helped
Add group-aware representations to ArcFaceArcFace with ResNet-100 · IJB-CTAR@FAR=1e-6: 0.8625 -> 0.9053Helped
Add group-aware representations to ArcFaceArcFace with ResNet-100 · AgeDB-30verification accuracy (%): 98.28 -> 98.28No clear effect
Use 32 groups rather than the baseline system (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.3828 -> 0.4678Helped
Add self-grouping loss (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.4468 -> 0.4678Helped
Use self-distributed rather than naive group labels (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.4535 -> 0.4678Helped
Use soft rather than hard group ensemble (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.4439 -> 0.4678Helped
Use soft rather than hard group ensemble (ablation)GroupFace · IJB-BGFLOPS: 24.4G -> 24.5GHurt
Concatenate rather than aggregate representations (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.4678 -> 0.4745Helped
Concatenate rather than aggregate representations (ablation)GroupFace · IJB-BTAR@FAR=1e-5: 0.9115 -> 0.8999Hurt
Add group-aware similarity at evaluation (ablation)GroupFace · IJB-BTAR@FAR=1e-6: 0.4678 -> 0.5212Helped
Add GroupFace to a ResNet-34 backbone (ablation)ResNet-34 · IJB-BTAR@FAR=1e-6: 0.3962 -> 0.4361Helped

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