A soft-weighted contrastive loss using rank-gap affinity and disparity terms learns globally consistent ordinal embeddings and reaches SOTA on age, BIQA, and BVQA benchmarks.
The gradient norms differ moderately in scale but remain consistently bounded and settle quickly into steady ranges
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Contrastive Order Learning: A General Framework for Ordinal Regression
A soft-weighted contrastive loss using rank-gap affinity and disparity terms learns globally consistent ordinal embeddings and reaches SOTA on age, BIQA, and BVQA benchmarks.