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Anomaly Detection

Expecting The Unexpected: Towards Broad Out-Of-Distribution Detection
Deployed machine learning systems require some mechanism to detect out-of-distribution (OOD) inputs. Existing research mainly focuses …
Leveraging Activation Patterns to Define Classifiers Able to Detect and Reject Anomalies
In this work, we introduce models that perform comparably with state-of-the-art alternatives in terms of prediction accuracy while …