自动发现模型在特定数据上的系统性错误，提升调试效率与修复能力。
HiBug2: Efficient and Interpretable Error Slice Discovery for Comprehensive Model Debugging
- 通过可解释的视觉属性生成，定位易出错样本。
- 高效枚举错误片段，克服组合爆炸难题。
- 可预测验证集外错误片段，适合模型可靠性研究者。
尽管深度学习模型在计算机视觉领域取得显著成功，但其常在特定数据子集上表现出系统性失效，称为错误片段。识别并缓解这些错误片段对提升模型在真实场景中的鲁棒性和可靠性至关重要。本文提出 HiBug2，一个自动化错误片段发现与模型修复框架。该框架首先通过可解释、结构化过程生成任务相关的视觉属性，突出显示易出错实例；随后采用高效片段枚举算法，系统性识别错误片段，克服了片段探索中的组合难题。此外，HiBug2 还拓展能力，可预测验证集外的错误片段，解决了以往方法的关键局限。在图像分类、姿态估计和目标检测等多个领域的大量实验表明，HiBug2 不仅提升了所识别错误片段的一致性与精度，还显著增强了模型修复能力。
原文摘要 · Abstract (English)
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 HiBug2, an automated framework for error slice discovery and model repair. HiBug2 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, HiBug2 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 HiBug2 not only improves the coherence and precision of identified error slices but also significantly enhances the model repair capabilities.
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