ContaminationAD: Anomaly detection with contaminated data

Anomaly detection is an essential part in industrial production which intends to identify anomaly samples and localize anomaly regions. Although current unsupervised anomaly detection methods have achieved excellent performance, they rely on clean and unlabeled normal samples. However, it is rarely satisfied in industrial scenarios and training with contaminated data is an inevitable challenge. To solve this problem, we propose an effective anomaly detection framework, named ContaminationAD. In our method, a pseudo-label generation strategy is introduced to evaluate the contamination levels of image patches and determines whether the sample is contaminated. Following this, a coarse localization of contaminated regions can be obtained and pseudo-labels are generated to guide text feature assignment for each sample. To concentrate on regions of interest while not neglecting fine-grained information, we design the Dual-Expert Network by incorporating routing mechanism to process visual feature adaptively. Based on this design, we propose semantic-driven segmentation weighting to facilitate anomaly localization. By applying routing weights and coarse localization when calculating the similarity between textual and local visual features, the training samples are dynamically weighted which enables the network to achieve a balance between the learning of normal and contaminated regions. Comprehensive experiments on the MVTecAD, VisA and BTAD datasets demonstrate that ContaminationAD outperforms the state-of-the-art anomaly detection methods. Notably, ContaminationAD exhibits strong robustness in the presence of data contamination, even when the contamination level is severe.