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：数据切片发现是一种新兴的机器学习模型验证技术，它通过识别和分析数据集中表现出较差性能的子组（通常具有独特的特征集或描述性元数据）来实现验证。然而，在验证涉及非结构化图像数据的视觉模型时，这种方法面临重大挑战，包括对额外元数据繁琐且昂贵的要求，以及解释性能不佳根本原因的复杂任务。为应对这些挑战，我们引入了AttributionScanner，一个创新的人机协同可视化分析系统，专为无元数据的数据切片发现而设计。我们的系统识别涉及常见模型行为的可解释数据切片，并通过归因马赛克设计可视化这些模式。我们的交互界面为用户提供了直观的指导，使其能够以最小努力检测、解释和标注主要的模型问题，例如伪相关性（模型偏差）和错误标记的数据。此外，系统采用先进的模型正则化技术来缓解检测到的问题并提升模型性能。通过涉及两个基准数据集的用例，结合定性和定量评估，证明了AttributionScanner在视觉模型验证中的显著有效性，最终有助于构建更可靠、更准确的模型。