Web: http://arxiv.org/abs/2209.09786

Sept. 21, 2022, 1:13 a.m. | Dario Mantegazza, Alessandro Giusti, Luca Maria Gambardella, Jérôme Guzzi

cs.CV updates on arXiv.org arxiv.org

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 …

anomaly anomaly detection arxiv detection mobile performance robots

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