Few-shot Anomaly Detection in Text with Deviation Learning

TL;DR

This paper introduces FATE, a novel few-shot learning framework for text anomaly detection that explicitly learns anomaly scores using deviation learning, effectively leveraging limited labeled anomalies for improved detection.

Contribution

The paper proposes a new end-to-end deep learning method that utilizes deviation learning and attention mechanisms to improve few-shot text anomaly detection, outperforming existing methods.

Findings

Achieves state-of-the-art performance on benchmark datasets.

Effectively leverages limited anomaly examples for improved detection.

Utilizes deviation learning to explicitly model anomaly scores.

Abstract

Most current methods for detecting anomalies in text concentrate on constructing models solely relying on unlabeled data. These models operate on the presumption that no labeled anomalous examples are available, which prevents them from utilizing prior knowledge of anomalies that are typically present in small numbers in many real-world applications. Furthermore, these models prioritize learning feature embeddings rather than optimizing anomaly scores directly, which could lead to suboptimal anomaly scoring and inefficient use of data during the learning process. In this paper, we introduce FATE, a deep few-shot learning-based framework that leverages limited anomaly examples and learns anomaly scores explicitly in an end-to-end method using deviation learning. In this approach, the anomaly scores of normal examples are adjusted to closely resemble reference scores obtained from a prior distribution. Conversely, anomaly samples are forced to have anomalous scores that considerably deviate from the reference score in the upper tail of the prior. Additionally, our model is optimized to learn the distinct behavior of anomalies by utilizing a multi-head self-attention layer and multiple instance learning approaches. Comprehensive experiments on several benchmark datasets demonstrate that our proposed approach attains a new level of state-of-the-art performance.