We study anomaly detection for the case when the normal class consists of more than one object category. This is an obvious generalization of the standard one-class anomaly detection problem. However, we show that jointly using multiple one-class anomaly detectors to solve this problem yields poorer results as compared to training a single one-class anomaly detector on all normal object categories together. We further develop a new anomaly detector called DeepMAD that learns compact distinguishing features by exploiting the multiple normal objects categories. This algorithm achieves higher AUC values for different datasets compared to two top performing one-class algorithms that either are trained on each normal object category or jointly trained on all normal object categories combined. In addition to theoretical results we present empirical results using the CIFAR-10, fMNIST, CIFAR-100, and a new dataset we developed called RECYCLE.

翻译：当普通类由多个对象类别组成时,我们研究异常点检测。这是对标准的单级异常点检测问题的明显概括化。然而,我们表明,联合使用多级单级异常探测器解决这一问题的结果比一起培训单一单级异常点检测器在所有正常对象类别上的结果要差。我们进一步开发了一个新的叫做DeepMAD的异常点检测器,该检测器通过利用多个正常对象类别来学习紧凑的区别特征。这一算法使不同数据集的AUC值更高,而两个表现最出色的单级算法,或者对每个正常对象类别进行培训,或者对所有正常对象类别进行联合培训。除了理论结果外,我们还利用CIFAR-10、FMIST、CIFAR-100和我们开发的称为RECYCLE的新数据集介绍了实证结果。