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

Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!