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

Exploring Disentangled Feature Representation Beyond Face Identification

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Fair and quality-aware face recognition · 174 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
disentangle features into identity-distilled and complementary identity-dispelled streamsD²AE trained on WebFace · LFWaccuracy: 98.93 -> 99.25Helped
disentangle features into identity-distilled and complementary identity-dispelled streamsD²AE trained on WebFace · LFWTPR@0.001FPR: 94.87 -> 96.80Helped
use the combined two-stream representation instead of the identity-distilling branch alone (ablation)D²AE · LFWidentity accuracy: 99.78 -> 99.80No clear effect
use the combined two-stream representation instead of the identity-distilling branch alone (ablation)D²AE · LFWTPR@0.001FPR: 99.63 -> 99.40Hurt
add the identity-dispelling branch to the identity-distilling branch (ablation)D²AE · CelebA and LFWAattribute accuracy: 79.78 -> 83.16Helped
add identity confusion loss L_H (ablation)D²AE-P · LFWidentity accuracy: 71.2 -> 64.13Hurt
add identity confusion loss L_H (ablation)D²AE-P · CelebA and LFWAattribute accuracy: 80.47 -> 81.99Helped
add adversarial identity classification loss L_I^adv (ablation)D²AE-P · LFWidentity accuracy: 67.13 -> 64.13Hurt
add adversarial identity classification loss L_I^adv (ablation)D²AE-P · CelebA and LFWAattribute accuracy: 78.32 -> 81.99Helped
combine identity-distilled and identity-dispelled features (ablation)D²AE · CelebA and LFWAattribute accuracy: 81.99 -> 83.16Helped

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