Weakly Supervised Anomaly Detection: A Survey

TL;DR

This survey comprehensively reviews weakly supervised anomaly detection methods across various data types, highlighting their definitions, algorithms, and future directions, supported by experiments and released resources.

Contribution

First comprehensive survey of WSAD methods categorizing them by supervision type and data modality, including formal definitions, algorithms, and future research directions.

Findings

Experimental results support effectiveness of selected WSAD methods.

Resources and source code are publicly available for future research.

Categorization aids in understanding and developing WSAD techniques.

Abstract

Anomaly detection (AD) is a crucial task in machine learning with various applications, such as detecting emerging diseases, identifying financial frauds, and detecting fake news. However, obtaining complete, accurate, and precise labels for AD tasks can be expensive and challenging due to the cost and difficulties in data annotation. To address this issue, researchers have developed AD methods that can work with incomplete, inexact, and inaccurate supervision, collectively summarized as weakly supervised anomaly detection (WSAD) methods. In this study, we present the first comprehensive survey of WSAD methods by categorizing them into the above three weak supervision settings across four data modalities (i.e., tabular, graph, time-series, and image/video data). For each setting, we provide formal definitions, key algorithms, and potential future directions. To support future research, we conduct experiments on a selected setting and release the source code, along with a collection of WSAD methods and data.