This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previous methods suffer from the high variance between class-wise accuracy which leads to not being applicable for all types of anomalies. The proposed method named RCALAD tries to solve this problem by introducing a novel discriminator to the structure, which results in a more efficient training process. Additionally, RCALAD employs a supplementary distribution in the input space to steer reconstructions toward the normal data distribution, effectively separating anomalous samples from their reconstructions and facilitating more accurate anomaly detection. To further enhance the performance of the model, two novel anomaly scores are introduced. The proposed model has been thoroughly evaluated through extensive experiments on six various datasets, yielding results that demonstrate its superiority over existing state-of-the-art models. The code is readily available to the research community at https://github.com/zahraDehghanian97/RCALAD.

翻译：本研究提出了一种面向实际应用的对抗式异常检测方法，通过利用生成对抗神经网络在重构误差中的循环一致性特性。现有方法存在类别间准确率差异大的问题，导致无法适用于所有类型的异常。所提出的RCALAD方法通过在框架中引入新型判别器来解决该问题，从而实现了更高效的训练过程。此外，RCALAD在输入空间中采用辅助分布，引导重构结果朝向正常数据分布，有效分离异常样本与其重构结果，从而促进更精准的异常检测。为进一步提升模型性能，我们引入了两种新型异常评分。通过在六个不同数据集上开展大量实验，对所提模型进行了全面评估，结果表明其优于现有最先进模型。相关代码已在https://github.com/zahraDehghanian97/RCALAD 公开，供研究社区使用。