D3O: Dynamic Distribution Distillation for Ordinal Regression
Ordinal regression is widely used in scenarios where labels are discrete yet inherently ordered. In practice, however, ordinal labels are often obtained by discretizing underlying continuous semantics through subjective human judgment, resulting in ambiguous boundaries and annotation noise. Such uncertainty challenges existing methods that rely on fixed supervision targets, which may reinforce biased ordering under subjective annotations. To address this limitation, we propose D3O, a dynamic distribution distillation framework that replaces static supervision with training-driven evolution of ordinal label distributions via self-distillation. Specifically, we introduce a contrastive ordin...
| Authors | Chunlai Dong, Yaojun Hu, Yuyang Xu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 26 July 2026 |
| Last updated at source | 26 July 2026 |
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