Anomaly Detection, Anomaly Segmentation, Novelty Detection, Out-of-Distribution Detection
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To the best of our knowledge, this is a first attempt to comparatively analyze the different evaluation frameworks used in the existing deep change detection methods.
The feature is collaboratively used with another feature that is the low-dimensional representation of multi-contrast images.
DRAM failure prediction is a vital task in AIOps, which is crucial to maintain the reliability and sustainable service of large-scale data centers.
Recent efforts towards video anomaly detection try to learn a deep autoencoder to describe normal event patterns with small reconstruction errors.
Learning to detect real-world anomalous events using video-level annotations is a difficult task mainly because of the noise present in labels.
In this paper, we propose a DiscRiminative-gEnerative duAl Memory (DREAM) anomaly detection model to take advantage of a few anomalies and solve data imbalance.
Semi-supervised methods of anomaly detection have seen substantial advancement in recent years.
Anomaly detection in visual data refers to the problem of differentiating abnormal appearances from normal cases.