  Anomaly detection, the task of identifying unusual samples in data, often
relies on a large set of training samples. In this work, we consider the
setting of few-shot anomaly detection in images, where only a few images are
given at training. We devise a hierarchical generative model that captures the
multi-scale patch distribution of each training image. We further enhance the
representation of our model by using image transformations and optimize
scale-specific patch-discriminators to distinguish between real and fake
patches of the image, as well as between different transformations applied to
those patches. The anomaly score is obtained by aggregating the patch-based
votes of the correct transformation across scales and image regions. We
demonstrate the superiority of our method on both the one-shot and few-shot
settings, on the datasets of Paris, CIFAR10, MNIST and FashionMNIST as well as
in the setting of defect detection on MVTec. In all cases, our method
outperforms the recent baseline methods.
