Anomaly detection (AD) is essential in identifying rare and often critical
events in complex systems, finding applications in fields such as network
intrusion detection, financial fraud detection, and fault detection in
infrastructure and industrial systems. While AD is typically treated as an
unsupervised learning task due to the high cost of label annotation, it is more
practical to assume access to a small set of labeled anomaly samples from
domain experts, as is the case for semi-supervised anomaly detection.
Semi-supervised and supervised approaches can leverage such labeled data,
resulting in improved performance. In this paper, rather than proposing a new
semi-supervised or supervised approach for AD, we introduce a novel algorithm
for generating additional pseudo-anomalies on the basis of the limited labeled
anomalies and a large volume of unlabeled data. This serves as an augmentation
to facilitate the detection of new anomalies. Our proposed algorithm, named
Nearest Neighbor Gaussian Mixup (NNG-Mix), efficiently integrates information
from both labeled and unlabeled data to generate pseudo-anomalies. We compare
the performance of this novel algorithm with commonly applied augmentation
techniques, such as Mixup and Cutout. We evaluate NNG-Mix by training various
existing semi-supervised and supervised anomaly detection algorithms on the
original training data along with the generated pseudo-anomalies. Through
extensive experiments on 57 benchmark datasets in ADBench, reflecting different
data types, we demonstrate that NNG-Mix outperforms other data augmentation
methods. It yields significant performance improvements compared to the
baselines trained exclusively on the original training data. Notably, NNG-Mix
yields up to 16.4%, 8.8%, and 8.0% improvements on Classical, CV, and NLP
datasets in ADBench. Our source code will be available at
this https URL

提出了一种名为最近邻高斯混合 (NNG-Mix) 的新算法，通过整合有标签和无标签数据来生成伪异常样本，用于改进半监督和监督异常检测算法，与常用的数据增强方法相比，在 57 个数据集上取得显著的性能提升，对于 ADBench 中的经典、计算机视觉和自然语言处理数据集，最多可提高 16.4%、8.8% 和 8.0%。

NNG-Mix: 利用伪异常生成改进半监督异常检测

NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly  Generation

Although deep learning has been applied to successfully address many data
mining problems, relatively limited work has been done on deep learning for
anomaly detection. Existing deep anomaly detection methods, which focus on
learning new feature representations to enable downstream anomaly detection
methods, perform indirect optimization of anomaly scores, leading to
data-inefficient learning and suboptimal anomaly scoring. Also, they are
typically designed as unsupervised learning due to the lack of large-scale
labeled anomaly data. As a result, they are difficult to leverage prior
knowledge (e.g., a few labeled anomalies) when such information is available as
in many real-world anomaly detection applications.
This paper introduces a novel anomaly detection framework and its
instantiation to address these problems. Instead of representation learning,
our method fulfills an end-to-end learning of anomaly scores by a neural
deviation learning, in which we leverage a few (e.g., multiple to dozens)
labeled anomalies and a prior probability to enforce statistically significant
deviations of the anomaly scores of anomalies from that of normal data objects
in the upper tail. Extensive results show that our method can be trained
substantially more data-efficiently and achieves significantly better anomaly
scoring than state-of-the-art competing methods.

本文提出了一种新颖的异常检测框架，利用少量标注的异常数据和先验概率，通过神经偏差学习完成异常得分的端到端学习，相比现有方法更高效地使用数据，并取得了显著更好的异常得分结果。