SymRankor: Symmetric Pairwise Ordinal Learning for Semi-Supervised Regression

Continuous-valued labels are often costly to obtain, motivating semi-supervised regression methods that learn from abundant unlabeled data. Confidence-based classification methods do not transfer directly, because a scalar regression output provides no normalized class confidence for selecting pseudo-labels. State-of-the-art methods such as RankUp address this mismatch with an auxiliary ranking classifier (ARC) over sample pairs, but its unlabeled pairwise objective uses cross-entropy and does not explicitly account for corrupted pseudo-orders. We propose SymRankor, which treats the weak-view order prediction as a potentially corrupted pairwise pseudo-label under a Bradley–Terry interpretation and applies a symmetric loss to the strong-view score difference. Under a theoretical label-flip model, the unlabeled-pair risk is a positive affine transform of its clean counterpart. This transformation preserves risk ordering and any attained argmin set without estimating the flip rate. Experiments across vision, audio, and text show that SymRankor improves upon its direct predecessor, with the clearest gains on audio and text. The symmetric objective remains competitive across the evaluated modalities even without confidence filtering. This study explicitly examines semi-supervised regression from this perspective. A chemical case study further suggests that the same ordinal objective can exploit coarse domain-derived ordinal information.