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.

本研究提出了一种面向SSAD的领域自适应方法，其中没有针对目标领域的异常数据。该方法利用变分自编码器和领域对抗网络生成无域变化的潜在变量，结合加权损失函数来近似理想损失函数，旨在解决SSAD在适应领域迁移方面的问题。实验结果表明该方法可以成功适应目标领域中缺少异常数据的情况。

半监督异常检测的零样本域适应