Age-aware loss reweighting and age-conditioned training reduce elderly recognition errors in facial expression models, even when age labels are automatically estimated, but the evidence rests on a small elderly benchmark.
A survey of predictive modeling on imbalanced domains
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Bridging the gap in FER: addressing age bias in deep learning
Age-aware loss reweighting and age-conditioned training reduce elderly recognition errors in facial expression models, even when age labels are automatically estimated, but the evidence rests on a small elderly benchmark.