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