  Recent Anomaly Detection techniques have progressed the field considerably
but at the cost of increasingly complex training pipelines. Such techniques
require large amounts of training data, resulting in computationally expensive
algorithms that are unsuitable for settings where only a small amount of normal
samples are available for training. We propose 'Few Shot anOMaly detection'
(FewSOME), a deep One-Class Anomaly Detection algorithm with the ability to
accurately detect anomalies having trained on 'few' examples of the normal
class and no examples of the anomalous class. We describe FewSOME to be of low
complexity given its low data requirement and short training time. FewSOME is
aided by pretrained weights with an architecture based on Siamese Networks. By
means of an ablation study, we demonstrate how our proposed loss, 'Stop Loss',
improves the robustness of FewSOME. Our experiments demonstrate that FewSOME
performs at state-of-the-art level on benchmark datasets MNIST, CIFAR-10,
F-MNIST and MVTec AD while training on only 30 normal samples, a minute
fraction of the data that existing methods are trained on. Moreover, our
experiments show FewSOME to be robust to contaminated datasets. We also report
F1 score and balanced accuracy in addition to AUC as a benchmark for future
techniques to be compared against. Code available;
https://github.com/niamhbelton/FewSOME.
