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.
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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 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: | https://discovery.ucl.ac.uk/id/eprint/10062495 |
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