%0 Generic %1 chalapathy2018anomaly %A Chalapathy, Raghavendra %A Menon, Aditya Krishna %A Chawla, Sanjay %D 2018 %K machinelearning %T Anomaly Detection using One-Class Neural Networks %U http://arxiv.org/abs/1802.06360 %X We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is suboptimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods.

@misc{chalapathy2018anomaly, abstract = {We propose a one-class neural network (OC-NN) model to detect anomalies in complex data sets. OC-NN combines the ability of deep networks to extract progressively rich representation of data with the one-class objective of creating a tight envelope around normal data. The OC-NN approach breaks new ground for the following crucial reason: data representation in the hidden layer is driven by the OC-NN objective and is thus customized for anomaly detection. This is a departure from other approaches which use a hybrid approach of learning deep features using an autoencoder and then feeding the features into a separate anomaly detection method like one-class SVM (OC-SVM). The hybrid OC-SVM approach is suboptimal because it is unable to influence representational learning in the hidden layers. A comprehensive set of experiments demonstrate that on complex data sets (like CIFAR and PFAM), OC-NN significantly outperforms existing state-of-the-art anomaly detection methods.}, added-at = {2018-11-21T00:08:31.000+0100}, author = {Chalapathy, Raghavendra and Menon, Aditya Krishna and Chawla, Sanjay}, biburl = {https://www.bibsonomy.org/bibtex/242c5eb1506991c91df34a0c4b53725b9/elfking}, description = {Anomaly Detection using One-Class Neural Networks}, interhash = {d8080b9b704d0111a25f5a215148c101}, intrahash = {42c5eb1506991c91df34a0c4b53725b9}, keywords = {machinelearning}, note = {cite arxiv:1802.06360Comment: Submitted, to KDD 2018 - London, United Kingdom. 19 - 23 August 2018}, timestamp = {2018-11-21T00:08:31.000+0100}, title = {Anomaly Detection using One-Class Neural Networks}, url = {http://arxiv.org/abs/1802.06360}, year = 2018 }