← 研踪 PaperTrailarXiv:2604.07383 · v3
SCOT: Soft Correspondence via Optimal Transport for Multi-Source Cross-City Transfer
首次提交 2026-04-08 10:42
版本更新 2026-11-08 22:31
雷达观测 2026-11-10 12:16
摘要
Cross-city transfer uses labeled data from well-instrumented cities to improve prediction in label-scarce ones. The task is difficult when cities have incompatible region partitions and no ground-truth correspondence. Existing methods either rely on heuristic anchors, which can create hubness, or align global distributions, which can over-mix heterogeneous regions. We propose SCOT (Soft Correspondence via Optimal Transport), an optimal transport-based framework for learning explicit, mass-controlled soft correspondences between unequal region sets. SCOT combines an optimal transport-weighted contrastive loss, a one-sided cycle regularizer, and a shared prototype hub for multi-source transfer. Across real-world cities and tasks, SCOT consistently outperforms strong baselines, reducing MAE and MAPE by 5-50%. Additional analyses show that the learned correspondences are selective, region-specific, and robust to heterogeneous settings.
作者备注：Accepted at NeurIPS 2026. 33 pages, 15 figures, 32 tables (including supplementary material)
观测到的版本
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v3 · 2026-11-08 22:31 · 最新观测版本
SCOT: Soft Correspondence via Optimal Transport for Multi-Source Cross-City Transfer
Cross-city transfer uses labeled data from well-instrumented cities to improve prediction in label-scarce ones. The task is difficult when cities have incompatible region partitions and no ground-truth correspondence. Existing methods either rely on heuristic anchors, which can create hubness, or align global distributions, which can over-mix heterogeneous regions. We propose SCOT (Soft Correspondence via Optimal Transport), an optimal transport-based framework for learning explicit, mass-controlled soft correspondences between unequal region sets. SCOT combines an optimal transport-weighted contrastive loss, a one-sided cycle regularizer, and a shared prototype hub for multi-source transfer. Across real-world cities and tasks, SCOT consistently outperforms strong baselines, reducing MAE and MAPE by 5-50%. Additional analyses show that the learned correspondences are selective, region-specific, and robust to heterogeneous settings.
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