DiffInf: Influence-Guided Diffusion for Supervision Alignment in Facial Attribute Learning
Facial attribute classification relies on large-scale annotated datasets in which many traits, such as age and expression, are inherently ambiguous and continuous but are discretized into categorical labels. Annotation inconsistencies arise from subjectivity and visual confounders such as pose, illumination, expression, and demographic variation, creating mismatch between images and assigned labels. These inconsistencies introduce supervision errors that impair representation learning and degrade downstream prediction. We introduce DiffInf, a self-influence--guided diffusion framework for mitigating annotation inconsistencies in facial attribute learning. We first train a baseline classif...
| Authors | Basudha Pal, Zhaoyang Wang, Rama Chellappa |
|---|---|
| Primary category | cs.CV |
| Published | 6 March 2026 |
| Last updated at source | 29 August 2026 |
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