Deep Anomaly Detection Using Geometric Transformations

NeurIPS 2018 Izhak GolanRan El-Yaniv

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)... (read more)

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