Semi-supervised anomaly detection~(SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer adapting to domain shifts, since anomalous data are unlikely to be available for the target domain in the training phase. To solve this problem, we propose a domain adaptation method for SSAD where no anomalous data are available for the target domain. First, we introduce a domain-adversarial network to a variational auto-encoder-based SSAD model to obtain domain-invariant latent variables. Since the decoder cannot reconstruct the original data solely from domain-invariant latent variables, we conditioned the decoder on the domain label. To compensate for the missing anomalous data of the target domain, we introduce an importance sampling-based weighted loss function that approximates the ideal loss function. Experimental results indicate that the proposed method helps adapt SSAD models to the target domain when no anomalous data are available for the target domain.

翻译：半监督异常检测（SSAD）是一项利用正常数据和有限异常数据进行训练的任务。在实际场景中，由于目标域在训练阶段难以提供异常数据，SSAD方法难以适应域偏移。为解决该问题，我们提出了一种面向SSAD的域自适应方法，其中目标域无需任何异常数据。首先，我们在基于变分自编码器的SSAD模型中引入域对抗网络，以获取域不变潜变量。由于解码器无法仅通过域不变潜变量重构原始数据，我们以域标签为条件对解码器进行约束。为弥补目标域异常数据的缺失，我们引入基于重要性采样的加权损失函数来近似理想损失函数。实验结果表明，当目标域无异常数据可用时，所提方法有助于SSAD模型适应目标域。