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Anomaly Detection (AD) is a critical task that involves identifyingobservations that do not conform to a learned model of normality. Prior work indeep AD is predominantly based on a familiarity hypothesis, where familiarfeatures serve as the reference in a pre-trained embedding space. While thisstrategy has proven highly successful, it turns out that it causes consistentfalse negatives when anomalies consist of truly novel features that are notwell captured by the pre-trained encoding. We propose a novel approach to ADusing explainability to capture such novel features as unexplained observationsin the input space. We achieve strong performance across a wide range ofanomaly benchmarks by combining familiarity and novelty in a hybrid approach.Our approach establishes a new state-of-the-art across multiple benchmarks,handling diverse anomaly types while eliminating the need for expensivebackground models and dense matching. In particular, we show that by takingaccount of novel features, we reduce false negative anomalies by up to 40% onchallenging benchmarks compared to the state-of-the-art. Our method givesvisually inspectable explanations for pixel-level anomalies.

Don't Miss Out on Novelty: Importance of Novel Features for Deep Anomaly Detection