A Robust Autoencoder Ensemble-Based Approach for Anomaly Detection in Text
This work addresses the underdeveloped area of anomaly detection in text, offering a novel approach that could benefit applications like content moderation or fraud detection, though it appears incremental by building on existing self-supervised methods.
The paper tackles anomaly detection in text by introducing a method for contaminating inlier classes with independent or contextual anomalies and proposing a robust autoencoder ensemble, achieving superior performance and robustness compared to recent works on both anomaly types across eight datasets.
Anomaly detection (AD) is a fast growing and popular domain among established applications like vision and time series. We observe a rich literature for these applications, but anomaly detection in text is only starting to blossom. Recently, self-supervised methods with self-attention mechanism have been the most popular choice. While recent works have proposed a working ground for building and benchmarking state of the art approaches, we propose two principal contributions in this paper: contextual anomaly contamination and a novel ensemble-based approach. Our method, Textual Anomaly Contamination (TAC), allows to contaminate inlier classes with either independent or contextual anomalies. In the literature, it appears that this distinction is not performed. For finding contextual anomalies, we propose RoSAE, a Robust Subspace Local Recovery Autoencoder Ensemble. All autoencoders of the ensemble present a different latent representation through local manifold learning. Benchmark shows that our approach outperforms recent works on both independent and contextual anomalies, while being more robust. We also provide 8 dataset comparison instead of only relying to Reuters and 20 Newsgroups corpora.