一句话：提出无需OOD先验知识或超参数调优的基尼系数分布外检测方法，提升ML软件系统鲁棒性。
ML模型已成为自动驾驶、医疗和金融等领域软件系统的核心组件，其鲁棒性直接影响系统性能与可靠性。当系统遇到与训练分布不同的分布外（OOD）数据时，性能会显著下降，因此有效的OOD检测机制对维持系统鲁棒性至关重要。现有OOD检测方法依赖用分布内和OOD数据调优的超参数，但在生产环境中预先定义OOD数据往往不可行，且这些方法对与分布内数据特征相似的OOD数据性能下降。本文提出一种基于基尼系数的OOD检测新方法，无需OOD数据先验知识，也无需超参数调优。在常见基准数据集上，该方法优于最大softmax概率（MSP）基线；对于在MNIST上训练的模型，在CIFAR10上的OOD检测率提升4%，在EMNIST上提升超过50%。
Machine learning (ML) models have become essential components in software systems across several domains, such as autonomous driving, healthcare, and finance. The robustness of these ML models is crucial for maintaining the software systems performance and reliability. A significant challenge arises when these systems encounter out-of-distribution (OOD) data, examples that differ from the training data distribution. OOD data can cause a degradation of the software systems performance. Therefore, an effective OOD detection mechanism is essential for maintaining software system performance and robustness. Such a mechanism should identify and reject OOD inputs and alert software engineers. Current OOD detection methods rely on hyperparameters tuned with in-distribution and OOD data. However, defining the OOD data that the system will encounter in production is often infeasible. Further, the performance of these methods degrades with OOD data that has similar characteristics to the in-distribution data. In this paper, we propose a novel OOD detection method using the Gini coefficient. Our method does not require prior knowledge of OOD data or hyperparameter tuning. On common benchmark datasets, we show that our method outperforms the existing maximum softmax probability (MSP) baseline. For a model trained on the MNIST dataset, we improve the OOD detection rate by 4% on the CIFAR10 dataset and by more than 50% for the EMNIST dataset.
@inproceedings{paperbot8424,
title = {Towards Robust ML-enabled Software Systems: Detecting Out-of-Distribution data using Gini Coefficients},
author = {Hala Abdelkader and Jean-Guy Schneider and Mohamed Abdelrazek and Priya Rani and Rajesh Vasa},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695312}
}