一种在不可靠、有噪声和不一致标签上评估模型的新方法：自适应分辨率标签聚合（ARLA）
A Novel Method to Evaluate Models on Unreliable, Noisy and Inconsistent Labels: Adaptive Resolution Label Aggregation (ARLA)
- Institute of Information Science Cologne University of Applied Sciences（科隆应用科学大学信息科学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对深度学习分割模型在不可靠标签上评估困难的问题，提出自适应分辨率标签聚合（ARLA）方法，通过动态调整标签和模型预测分辨率，能更好分析模型行为，为评估分割模型提供新途径。
AI中文摘要：
标签对于深度学习分割模型的训练和评估至关重要，但在类别边界处往往不一致、有噪声或模糊。许多方法用于在弱标签上训练模型，而目前几乎没有方法便于在不可靠标签上评估模型。因此引入“自适应分辨率标签聚合”（ARLA）方法，它在计算评估指标前，于推理时动态调整标签和模型预测的分辨率。通过实际应用于真实洪水预测模型展示了ARLA能更好分析模型行为，克服了森林区域标签不一致和重云覆盖区域标签错误问题。该工作提出了评估分割模型的新方法，参数可调以适应聚合分辨率与标签精度或标签噪声水平。本质上，ARLA利用标签封装的信息并最小化标签错误，从噪声中提取模型真实性能的更清晰信号。
英文摘要：
Labels are critical for both training and evaluating deep learning segmentation models, but are often inconsistent, noisy, or ambiguous at class boundaries. Many approaches have been developed to support training models on weak labels, but few to none currently exist to facilitate evaluating models on unreliable labels. We therefore introduce a method called "Adaptive Resolution Label Aggregation", or "ARLA", which dynamically adapts the resolution of both the label and the model prediction at inference time before the evaluation metrics are computed. We demonstrate how ARLA can be used to better analyse model behaviour with a practical application to a real flood prediction model, where ARLA was able to overcome issues with inconsistent labelling of forested areas and errors in labels within regions of heavy cloud cover. Our work presents a new approach to evaluating segmentation models, with adjustable parameters to adapt the aggregated resolution to the precision of the label or the level of label noise. Fundamentally, ARLA exploits the information encapsulated by a label but minimises the label error, extracting from the noise a clearer signal of a model's true performance.
