  We introduce a formalization and benchmark for the unsupervised anomaly
detection task in the distribution-shift scenario. Our work builds upon the
iWildCam dataset, and, to the best of our knowledge, we are the first to
propose such an approach for visual data. We empirically validate that
environment-aware methods perform better in such cases when compared with the
basic Empirical Risk Minimization (ERM). We next propose an extension for
generating positive samples for contrastive methods that considers the
environment labels when training, improving the ERM baseline score by 8.7%.
