This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significantly in the presence of a domain shift, difficult to avoid in a real-world setting. While UDA has contributed to solving this issue in binary and multi-class classification, such a strategy is ill-posed in UAD. This might be explained by the unsupervised nature of the two tasks, namely, domain adaptation and anomaly detection. Herein, we first formulate this problem that we call the two-fold unsupervised curse. Then, we propose a pioneering solution to this curse, considered intractable so far, by assuming that anomalies are rare. Specifically, we leverage clustering techniques to identify a dominant cluster in the target feature space. Posed as the normal cluster, the latter is aligned with the source normal features. Concretely, given a one-class source set and an unlabeled target set composed mostly of normal data and some anomalies, we fit the source features within a hypersphere while jointly aligning them with the features of the dominant cluster from the target set. The paper provides extensive experiments and analysis on common adaptation benchmarks for anomaly detection, demonstrating the relevance of both the newly introduced paradigm and the proposed approach. The code will be made publicly available.

翻译：本文首次提出了一种面向无监督异常检测的完全无监督域适应框架。在现实场景中难以避免的域偏移会导致无监督异常检测技术的性能显著下降。尽管无监督域适应技术在二元和多类分类中已成功解决此类问题，但该策略在无监督异常检测中却难以直接应用。这或许可归因于域适应与异常检测这两个任务本身都具有无监督特性。本文首先将这一难题形式化为"双重无监督困境"，随后通过假设异常样本具有稀缺性，针对这一迄今被视为棘手的困境提出了开创性解决方案。具体而言，我们利用聚类技术在目标特征空间中识别主导聚类，将其定义为正常类簇，并与源域正常特征进行对齐。在实际操作中，给定包含单类正常样本的源数据集以及由多数正常数据和少量异常构成的未标注目标数据集，我们将源域特征约束在超球体内，同时将其与目标数据集主导聚类的特征进行联合对齐。本文通过在异常检测领域常用的域适应基准数据集上进行大量实验与分析，验证了新提出范式及方法的有效性。相关代码将公开提供。