To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging.
To calculate the TPR in the objective function, we consider that the set of anomalous sounds is the complementary set of normal sounds and simulate anomalous sounds by using a rejection sampling algorithm.
To the best of our knowledge, our method is the first data augmentation technique focused on improving performance in unsupervised anomaly detection.
To the best of our knowledge, this is the first work to study the interpretability of deep learning in anomaly detection.
At the test stage, the learned memory will be fixed, and the reconstruction is obtained from a few selected memory records of the normal data.
In this paper we present an analysis of a general algorithm for band selection based on higher order cumulants.
Reliably modeling normality and differentiating abnormal appearances from normal cases is a very appealing approach for detecting pathologies in medical images.
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.