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Transfer representation-learning for anomaly detection

Andrews, J; Tanay, T; Morton, EJ; Griffin, LD; (2016) Transfer representation-learning for anomaly detection. In: Proceedings of the 33rd International Conference on Machine Learning. JMLR: New York, NY, USA. Green open access

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Abstract

We evaluate transfer representation-learning for anomaly detection using convolutional neural networks by: (i) transfer learning from pretrained networks, and (ii) transfer learning from an auxiliary task by defining sub-categories of the normal class. We empirically show that both approaches offer viable representations for the task of anomaly detection, without explicitly imposing a prior on the data.

Type: Proceedings paper
Title: Transfer representation-learning for anomaly detection
Event: 33rd International Conference on Machine Learning
Open access status: An open access version is available from UCL Discovery
Publisher version: http://proceedings.mlr.press/v48/
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10062495
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