Despite the significant success of deep learning models in computer vision, they often exhibit systematic failures on specific data subsets, known as error slices. Identifying and mitigating these error slices is crucial to enhancing model robustness and reliability in real-world scenarios. In this paper, we introduce DebugAgent, an automated framework for error slice discovery and model repair. DebugAgent first generates task-specific visual attributes to highlight instances prone to errors through an interpretable and structured process. It then employs an efficient slice enumeration algorithm to systematically identify error slices, overcoming the combinatorial challenges that arise during slice exploration. Additionally, DebugAgent extends its capabilities by predicting error slices beyond the validation set, addressing a key limitation of prior approaches. Extensive experiments across multiple domains, including image classification, pose estimation, and object detection - show that DebugAgent not only improves the coherence and precision of identified error slices but also significantly enhances the model repair capabilities.

翻译：尽管深度学习模型在计算机视觉领域取得了显著成功，但其在特定数据子集（即错误切片）上常表现出系统性故障。识别并缓解这些错误切片对于提升模型在真实场景中的鲁棒性与可靠性至关重要。本文提出DebugAgent，一种用于错误切片发现与模型修复的自动化框架。该框架首先通过可解释的结构化过程生成任务相关的视觉属性，以突出易产生错误的实例；随后采用高效的切片枚举算法系统性地识别错误切片，从而克服切片探索过程中产生的组合爆炸问题。此外，DebugAgent能够预测验证集之外的数据错误切片，突破了现有方法的关键局限。在图像分类、姿态估计与目标检测等多个领域的广泛实验表明，DebugAgent不仅能提升所识别错误切片的一致性与精确度，还可显著增强模型的修复能力。