In this paper , we investigate a complementary method where we train models to detect unmodeled data by learning cues for whether an input is unmodeled . While it is difficult to model the full data distribution , we can learn effective heuristics for detecting out - of - distribution inputs by exposing the model to OOD examples , thus learning a more conservative concept of the inliers and enabling the detection of novel forms of anomalies . We propose leveraging diverse , realistic datasets for this purpose , with a method we call Outlier Exposure ( OE ) . OE provides a simple and effective way to consistently improve existing methods for OOD detection .