Deep Anomaly Detection Using Geometric Transformations

We consider the problem of anomaly detection in images, and present a new detectiontechnique. Givenasampleofimages,allknowntobelongtoa"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).