Anomaly Detection Under Contaminated Data With Contamination-Immune Bidirectional GANs

Anomaly detection aims to detect instances that deviate significantly from the majority. Due to the difficulties of collecting a large amount of anomalies in practice, existing methods generally assume the availability of a clean normal dataset and leverage it to detect anomalies by characterizing the normality of normal samples. However, for many application scenarios, collecting a normal dataset that is sufficiently clean is not easy. What is often observed is that a small amount of anomalies are often falsely mixed into the normal dataset, resulting in a contaminated dataset. Obviously, the contamination in the normal dataset could significantly compromise the model's ability to detect anomalies. To alleviate this issue, two contamination-immune bidirectional generative adversarial networks (BiGAN) are developed, which can learn the probability distribution of normal samples from a contaminated dataset under some mild conditions. Rigorous proofs are provided to guarantee the theoretical correctness of the proposed models. Thanks to the removing of negative influences from the contamination samples, the proposed contamination-immune models can thus be applied to detect anomalies accurately for the scenarios with contaminated datasets. Extensive experimental results show that the proposed method outperforms the current state-of-the-art (SOTA) ones significantly under the scenarios with contaminated training datasets. IEEE