AI 中文总结
针对合成退化数据筛选，提出受控可约简退化差距（cRDG）指标及筛选方法，通过预算匹配探针分离退化增益与干净干扰，在语义分割和显著目标检测上提升预测器性能。
AI 中文摘要
为密集预测任务选择合成退化数据，需要估计其训练效用，即在有限训练预算下这些数据带来的泛化增益。干净图像与退化图像构成的双胞胎样本共享内容和标签，这启发了一种基于短期训练能在多大程度上减少由退化引起的额外误差的评分方法。然而，当干净性能下降时，该差距也可能缩小。仅测量退化图像上的改进可以避免这一混淆因素，但仍会将同等训练量在干净数据上本可产生的进步归功于退化数据。我们针对由退化类型和严重程度定义的区域，提出了受控可约简退化差距（cRDG）。从同一初始检查点出发，cRDG运行两个预算匹配的探针实验，二者仅在一个增强槽位上不同，该槽位要么施加合成退化，要么施加干净增强。得分是相对于干净对照探针在留出退化图像上的增益。对干净性能的损害是单独的可行性约束。cRDG揭示了一个可修正的严重程度区间，在该区间内，在可用预算下对退化数据进行训练能产生高受控增益，且该区间随预测器、初始检查点和训练预算而变化。可约简区间筛选方法（Curation of Reducible Bands）利用cRDG选择合成数据而不改变预测器。在语义分割和显著目标检测任务上，该方法在匹配的合成数据预算和训练计划下提升了代表性预测器的性能，可扩展到现有数据生成流程，并保持干净性能不下降。代码和支持材料将公开发布。
英文摘要
Selecting synthetic degradations for dense prediction requires an estimate of their training utility, the generalization gain they bring under a finite training budget. Clean and degraded twins share content and labels, suggesting a score based on how much short training reduces the excess error caused by degradation. However, this gap can also shrink when clean performance deteriorates. Measuring the improvement on degraded images alone avoids that confound, but it still credits progress that the same amount of clean training would have produced. We propose the \textbf{controlled Reducible Degradation Gap} (cRDG) for regions defined by degradation type and severity. From a common checkpoint, cRDG runs two budget-matched probes that differ only in one augmentation slot, which holds either a synthetic degradation or a clean augmentation. The score is the gain on held-out degraded images relative to the clean-control probe. Clean harm is a separate feasibility constraint. cRDG reveals a correctable severity band in which training on the degradation yields high controlled gain under the available budget, and the band moves with the predictor, the starting checkpoint, and the training budget. \textbf{Curation of Reducible Bands} (\method) uses cRDG to select synthetic data without changing the predictor. On semantic segmentation and salient object detection, \method{} improves representative predictors under matched synthetic-data budgets and training schedules, extends to existing data-generation pipelines, and preserves clean performance. Code and supporting materials will be publicly released.
Comments17 pages, 4 figures, 9 tables
