Anomaly detection, a critical facet in data analysis, involves identifying
patterns that deviate from expected behavior. This research addresses the
complexities inherent in anomaly detection, exploring challenges and adapting
to sophisticated malicious activities. With applications spanning
cybersecurity, healthcare, finance, and surveillance, anomalies often signify
critical information or potential threats. Inspired by the success of Anomaly
Generative Adversarial Network (AnoGAN) in image domains, our research extends
its principles to tabular data. Our contributions include adapting AnoGAN's
principles to a new domain and promising advancements in detecting previously
undetectable anomalies. This paper delves into the multifaceted nature of
anomaly detection, considering the dynamic evolution of normal behavior,
context-dependent anomaly definitions, and data-related challenges like noise
and imbalances.

通过将 AnoGAN 的原理应用于表格数据，本研究在异常检测领域做出了新的贡献，并在检测以前无法检测到的异常方面取得了有希望的进展。

Tabular 数据的 AnoGAN: 一种新颖的异常检测方法

AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection

The performance of machine learning models heavily depends on the quality of
input data, yet real-world applications often encounter various data-related
challenges. One such challenge could arise when curating training data or
deploying the model in the real world - two comparable datasets in the same
domain may have different distributions. While numerous techniques exist for
detecting distribution shifts, the literature lacks comprehensive approaches
for explaining dataset differences in a human-understandable manner. To address
this gap, we propose a suite of interpretable methods (toolbox) for comparing
two datasets. We demonstrate the versatility of our approach across diverse
data modalities, including tabular data, language, images, and signals in both
low and high-dimensional settings. Our methods not only outperform comparable
and related approaches in terms of explanation quality and correctness, but
also provide actionable, complementary insights to understand and mitigate
dataset differences effectively.

通过提出一套可解释性方法对比两个数据集，我们展示了这种方法在各种数据模式下的多功能性，不仅在解释质量和正确性方面优于相关方法，而且能够提供行动性的、互补的见解来有效理解和减轻数据集的差异。