… Aiming at this problem, an improved SVDD model called deep structure preservation SVDD
… deep support vector data description model for anomaly detection. Different from the basic …

… the benefits of classical anomaly detection methods and deep networks but also avoid the
… Deep SVDD based on variational autoencoder (Deep SVDD-VAE) to detect the anomaly. In …

… aims to locate the anomaly on the pixel level. Support vector data description (SVDD) is a
long-standing algorithm used for an anomaly detection, and we extend its deep learning …

… anomaly detection benchmark datasets, we demonstrate that our method is on par or
outperforms shallow, hybrid, and deep … as Deep SVDD for the unlabeled data in our Deep SAD …

… an SVDD-based anomaly detection approach which prevents the hypersphere collapse
problem that other deep SVDD … 2) In our model, unlike other SVDD-based deep models, the …

… In this section, we propose the deep fair SVDD model for deep anomaly detection … deep
fair SVDD with two popular deep anomaly detection methods: deep SVDD [33] and deep …

… the description of anomalies. To overcome the problems, we propose a novel semantic-enhanced
anomaly detection method based on deep Support Vector Data Description (SVDD). A …

… -world anomaly detection applications. This paper introduces a novel anomaly detection
framework … learning with distance-based detectors, while deep Support Vector Data Description (…

… testing time, where the linear kernel PSVM outperforms SVDD, while for the RBF kernel SVDD
… Consequently, hereafter only the results for PSVM with the linear kernel and SVDD with …

… In this paper we introduce a new anomaly detection method—Deep … With Deep SVDD,
we build on the kernel-based SVDD and minimum volume estimation by finding a data-enclosing …