论文标题
一种改善移动机器人视觉异常检测性能的离群曝光方法
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
论文作者
论文摘要
我们考虑为移动机器人构建视觉异常检测系统的问题。标准异常检测模型是使用仅由非异常数据组成的大型数据集训练的。但是，在机器人技术应用中，通常可以使用（可能很少）的异常示例。我们解决了利用这些数据以通过与Real-NVP损失共同使辅助外离群损失损失的最小化来提高实际NVP异常检测模型的性能的问题。我们在新的数据集（作为补充材料）上执行定量实验，旨在在室内巡逻方案中进行异常检测。在不相交的测试集上，我们的方法优于替代方案，并表明即使少数异常框架也会实现重大的性能改善。
We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements.
