  We consider the problem of anomaly detection in images, and present a new
detection technique. Given a sample of images, all known to belong to a
"normal" class (e.g., dogs), we show how to train a deep neural model that can
detect out-of-distribution images (i.e., non-dog objects). The main idea behind
our scheme is to train a multi-class model to discriminate between dozens of
geometric transformations applied on all the given images. The auxiliary
expertise learned by the model generates feature detectors that effectively
identify, at test time, anomalous images based on the softmax activation
statistics of the model when applied on transformed images. We present
extensive experiments using the proposed detector, which indicate that our
algorithm improves state-of-the-art methods by a wide margin.
