保形校准迁移
Conformal Calibration Transfer
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中文总结 AI 辅助
针对标定与部署数据不可交换的迁移场景，提出传输保形校准（TCC）方法，通过配对数据迁移标定并校正失配，实现无需目标标签的可靠覆盖保证。
中文摘要 AI 辅助
保形预测在标定数据与部署数据可交换的条件下，将点预测转换为具有覆盖保证的集合预测。我们研究保形校准迁移问题，即当标定数据仅在源空间中可用，而预测集合需要在通过无标签配对观测（如配对模态或传感器变化）与源空间关联的目标空间中生成时，上述可交换性要求不再成立。我们提出传输保形校准（TCC）方法：利用配对数据将带标签的源标定数据迁移到目标空间，然后仅使用无标签的目标输入来校正迁移后的残余失配。我们通过两种互补方法实现该校正：TCC-KS，使用无标签的不确定性替代指标检测失配并保守地调整校准；以及加权TCC，在权重稳定时，将迁移后的标定数据向目标域重新加权，以提高效率。我们提供了有限样本的目标域覆盖保证，该保证适应于可观测的失配度量。在CIFAR-100-C、Tiny-ImageNet-C和SEN12MS数据集上，我们展示了在没有目标域标签标定数据的情况下可靠的目标域覆盖迁移，并提供了可预测何时需要校正的无标签诊断方法。
英文摘要
Conformal prediction converts point predictions into set-valued predictions with coverage guarantees under exchangeability between calibration and deployment data. We study conformal calibration transfer, where this requirement fails because labeled calibration is available only in a source space, while prediction sets are needed in a target space linked to the source through unlabeled paired observations (e.g., paired modalities or sensor changes). We propose Transported Conformal Calibration (TCC): we transport labeled source calibration into the target space using the paired data, and then correct residual post-transport mismatch using only unlabeled target inputs. We instantiate this correction with two complementary methods: TCC-KS, which uses a label-free uncertainty surrogate to detect mismatch and adjust calibration conservatively, and weighted-TCC, which reweights transported calibration toward the target domain for improved efficiency when weights are stable. We provide finite-sample target-domain coverage guarantees that adapt to an observable measure of mismatch. Across CIFAR-100-C, Tiny-ImageNet-C, and SEN12MS, we show reliable target-domain coverage transfer without labeled target calibration data, with label-free diagnostics that predict when correction is needed.
发表机构
- Technical University of Darmstadt（达姆施塔特工业大学）
- Tongji University（同济大学）
机构由 AI 辅助整理，请以论文原文为准。
