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From a safety perspective, a machine learning method embedded in real-worldapplications is required to distinguish irregular situations. For this reason,there has been a growing interest in the anomaly detection (AD) task. Since wecannot observe abnormal samples for most of the cases, recent AD methodsattempt to formulate it as a task of classifying whether the sample is normalor not. However, they potentially fail when the given normal samples areinherited from diverse semantic labels. To tackle this problem, we introduce alatent class-condition-based AD scenario. In addition, we propose aconfidence-based self-labeling AD framework tailored to our proposed scenario.Since our method leverages the hidden class information, it successfully avoidsgenerating the undesirable loose decision region that one-class methods suffer.Our proposed framework outperforms the recent one-class AD methods in thelatent multi-class scenarios.

What is Wrong with One-Class Anomaly Detection?