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

• URL: http://arxiv.org/abs/2210.03103v1
• Date: Thu, 6 Oct 2022 17:52:33 GMT
• Title: Env-Aware Anomaly Detection: Ignore Style Changes, Stay True to Content!
• Authors: Stefan Smeu, Elena Burceanu, Andrei Liviu Nicolicioiu, Emanuela Haller
• Abstract summary: We introduce a formalization and benchmark for the unsupervised anomaly detection task in the distribution-shift scenario. We empirically validate that environment-aware methods perform better in such cases when compared with the basic Empirical Risk Minimization (ERM) We propose an extension for generating positive samples for contrastive methods that considers the environment labels when training, improving the baseline score by 8.7%.
• Score: 6.633524353120579
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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