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

TL;DR

This paper introduces a hybrid anomaly detection method that leverages explainability to identify novel features, significantly reducing false negatives and achieving state-of-the-art results across diverse benchmarks.

Contribution

It presents a novel hybrid approach combining familiarity and novelty detection using explainability, improving anomaly detection performance and interpretability.

Findings

Reduces false negatives by up to 40% on challenging benchmarks.

Establishes new state-of-the-art performance across multiple datasets.

Provides visually inspectable pixel-level explanations.

Abstract

Anomaly Detection (AD) is a critical task that involves identifying observations that do not conform to a learned model of normality. Prior work in deep AD is predominantly based on a familiarity hypothesis, where familiar features serve as the reference in a pre-trained embedding space. While this strategy has proven highly successful, it turns out that it causes consistent false negatives when anomalies consist of truly novel features that are not well captured by the pre-trained encoding. We propose a novel approach to AD using explainability to capture such novel features as unexplained observations in the input space. We achieve strong performance across a wide range of anomaly 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 expensive background models and dense matching. In particular, we show that by taking account of novel features, we reduce false negative anomalies by up to 40% on challenging benchmarks compared to the state-of-the-art. Our method gives visually inspectable explanations for pixel-level anomalies.