Transfer representation-learning for anomaly detection

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.

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