New D3O framework enhances ordinal regression with dynamic label distillation

Researchers have introduced D3O, a novel framework for ordinal regression that dynamically adjusts label distributions during training through self-distillation. This approach aims to overcome challenges posed by subjective human judgment in labeling, which often leads to ambiguous boundaries and annotation noise. D3O incorporates a module that enhances label distributions by leveraging vision-language alignment to capture inter-class ambiguity and instance-level uncertainty. Additionally, a cross-layer distillation mechanism ensures consistent ordinal structure across network layers. Experiments on four ordinal regression tasks show D3O's superior performance, especially when dealing with class imbalance and noisy supervision. AI

IMPACT Introduces a novel approach to handle noisy and ambiguous labels in ordinal regression tasks, potentially improving model robustness.

RANK_REASON The cluster contains a research paper detailing a new method for ordinal regression. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• D3O
• DagsHub
• Gotit.pub
• Hugging Face
• Litmaps
• ScienceCast
• Scite

• paper
• other

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →