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Research map / Label-efficient and robust learning / Recognition and long-tail

FreeEnricher: Enriching Face Landmarks without Additional Cost

AAAI 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Margin-based face recognition losses · 3 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add FreeEnricher to ADNet during trainingADNet · Enriched 300WNME_point: 3.21 -> 3.06Helped
Add FreeEnricher to ADNet during trainingADNet · Enriched 300WNME_edge: 1.18 -> 0.98Helped
Add FreeEnricher to ADNet during trainingADNet · 300WNME: 2.93 -> 2.87Helped
Add FreeEnricher to ADNet during trainingADNet · WFLWNME: 4.14 -> 4.10Helped
Regress offsets with FreeEnricher instead of using random offsets (ablation)FreeEnricher offset refinement · 300W TestsetME: 4.03 -> 1.92Helped
Regress offsets with FreeEnricher instead of using random offsets (ablation)FreeEnricher offset refinement · WFLW TestsetME: 3.96 -> 1.89Helped
Use B-spline rather than line interpolation for initialization (ablation)FreeEnricher without other operators · Enriched 300WNME: 3.18 -> 3.11Helped
Add patch normalizing to line initialization (ablation)FreeEnricher · Enriched 300WNME: 3.18 -> 3.14Helped
Add index embedding to line initialization (ablation)FreeEnricher · Enriched 300WNME: 3.18 -> 3.16Helped
Add quality scoring to line initialization and patch normalizing (ablation)FreeEnricher · Enriched 300WNME: 3.14 -> 3.11Helped
Add index embedding to the configuration with line initialization, patch normalizing, and quality scoring (ablation)FreeEnricher · Enriched 300WNME: 3.11 -> 3.06Helped
Increase enriching density from 1 to 10 (ablation)FreeEnricher · Enriched 300WNME: 2.91 -> 3.14Hurt

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