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Learning Deep Hierarchical Features with Spatial Regularization for One-Class Facial Expression Recognition

AAAI 2023 · Label-efficient and robust learning › Recognition and long-tail · approach: Uncertainty-aware facial expression recognition · 5 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use HS-OCFER instead of MKDOne-class FER · CFEEmean AUC: 64.82 -> 71.37Helped
Use HS-OCFER instead of IGDOne-class FER · ExpWmean AUC: 55.05 -> 58.17Helped
Add feature hierarchy alone (ablation)Basic HS-OCFER model · CFEEmean AUC: 51.41 -> 51.28Hurt
Add feature hierarchy alone (ablation)Basic HS-OCFER model · ExpWmean AUC: 53.46 -> 53.80Helped
Add compact space alone (ablation)Basic HS-OCFER model · CFEEmean AUC: 51.41 -> 57.54Helped
Add compact space alone (ablation)Basic HS-OCFER model · ExpWmean AUC: 53.46 -> 54.28Helped
Add feature hierarchy and spatial regularization (ablation)Basic HS-OCFER model · CFEEmean AUC: 51.41 -> 56.22Helped
Add feature hierarchy and compact space (ablation)Basic HS-OCFER model · CFEEmean AUC: 51.41 -> 65.26Helped
Add feature hierarchy, spatial regularization, and compact space (ablation)Basic HS-OCFER model · CFEEmean AUC: 51.41 -> 71.37Helped
Add feature hierarchy, spatial regularization, and compact space (ablation)Basic HS-OCFER model · ExpWmean AUC: 53.46 -> 58.17Helped
Fuse reconstruction quality and feature compactness for inference (ablation)HS-OCFER · CFEEmean AUC: 69.04±0.42; 70.42±0.60 -> 71.37±0.28Helped
Fuse reconstruction quality and feature compactness for inference (ablation)HS-OCFER · ExpWmean AUC: 57.43±0.65; 57.82±0.66 -> 58.17±0.14Helped

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