MeLIAD: Interpretable Few-Shot Anomaly Detection with Metric Learning and Entropy-based Scoring

TL;DR

MeLIAD is a new interpretable few-shot anomaly detection method that uses metric learning and entropy-based scoring to identify and visualize anomalies without prior distribution assumptions, requiring minimal anomaly samples.

Contribution

It introduces a novel entropy-based scoring component and a joint loss function for interpretable, few-shot anomaly detection based on metric learning, without relying on data augmentation.

Findings

Outperforms state-of-the-art methods in detection accuracy

Provides visual interpretability of anomalies

Effective with few anomaly samples without augmentation

Abstract

Anomaly detection (AD) plays a pivotal role in multimedia applications for detecting defective products and automating quality inspection. Deep learning (DL) models typically require large-scale annotated data, which are often highly imbalanced since anomalies are usually scarce. The black box nature of these models prohibits them from being trusted by users. To address these challenges, we propose MeLIAD, a novel methodology for interpretable anomaly detection, which unlike the previous methods is based on metric learning and achieves interpretability by design without relying on any prior distribution assumptions of true anomalies. MeLIAD requires only a few samples of anomalies for training, without employing any augmentation techniques, and is inherently interpretable, providing visualizations that offer insights into why an image is identified as anomalous. This is achieved by introducing a novel trainable entropy-based scoring component for the identification and localization of anomalous instances, and a novel loss function that jointly optimizes the anomaly scoring component with a metric learning objective. Experiments on five public benchmark datasets, including quantitative and qualitative evaluation of interpretability, demonstrate that MeLIAD achieves improved anomaly detection and localization performance compared to state-of-the-art methods.