Data slice finding is an emerging technique for validating machine learning (ML) models by identifying and analyzing subgroups in a dataset that exhibit poor performance, often characterized by distinct feature sets or descriptive metadata. However, in the context of validating vision models involving unstructured image data, this approach faces significant challenges, including the laborious and costly requirement for additional metadata and the complex task of interpreting the root causes of underperformance. To address these challenges, we introduce AttributionScanner, an innovative human-in-the-loop Visual Analytics (VA) system, designed for metadata-free data slice finding. Our system identifies interpretable data slices that involve common model behaviors and visualizes these patterns through an Attribution Mosaic design. Our interactive interface provides straightforward guidance for users to detect, interpret, and annotate predominant model issues, such as spurious correlations (model biases) and mislabeled data, with minimal effort. Additionally, it employs a cutting-edge model regularization technique to mitigate the detected issues and enhance the model's performance. The efficacy of AttributionScanner is demonstrated through use cases involving two benchmark datasets, with qualitative and quantitative evaluations showcasing its substantial effectiveness in vision model validation, ultimately leading to more reliable and accurate models.

翻译：数据片段发现是一种新兴的机器学习模型验证技术，通过识别和分析数据集中性能较差的子群组（通常具有独特特征集或描述性元数据）来验证模型。然而，在涉及非结构化图像数据的视觉模型验证场景中，该方法面临显著挑战：不仅需要耗费大量人力与成本获取额外元数据，还需对性能不佳的根本原因进行复杂解读。为应对这些挑战，我们提出AttributionScanner——一种创新的闭环可视化分析系统，专为无元数据的数据片段发现而设计。该系统可识别涉及共同模型行为的可解释数据片段，并通过归因马赛克设计实现模式可视化。交互式界面为用户提供直观引导，使其能够以最小工作量检测、解读并标注主流模型问题（如伪相关导致的模型偏差与标签错误）。此外，系统采用前沿的模型正则化技术缓解已发现问题，从而提升模型性能。通过在两个基准数据集上的用例分析，以及定性与定量评估，验证了AttributionScanner在视觉模型验证中的显著效能，最终助力构建更可靠、更精准的模型。