Outlier-Probability-Based Feature Adaptation for Robust Unsupervised Anomaly Detection on Contaminated Training Data

In the realm of large-scale industrial manufacturing, the precise detection of defective parts stands as a critical imperative. While current unsupervised anomaly detection algorithms exhibit commendable accuracy when applied to clean training datasets, their susceptibility to contaminated training data limits their real-world efficacy. In response to this challenge, this paper proposes a novel Outlier-Probability-Based Feature Adaptation (OPFA) network to realize robust unsupervised anomaly detection on contaminated training data. This method distinguishes itself by maintaining both high accuracy and robustness in the face of contaminated training data, enabling effective learning of discriminative features for anomaly detection. Specifically, the model enhances feature representations through the contraction of normal features and the contrast between normal and outlier features. Our methodology employs an iterative mechanism, featuring three core designs. First, outlier detection evaluates the outlier probabilities of current feature embeddings, providing a basis for subsequent improvements. Second, Gaussian Mixture Model (GMM) is leveraged to model the distributions of normal feature embeddings. Third, the adaptive network refines feature representations based on the GMM models and outlier scores of feature embeddings. Ablation experiments underscore the effectiveness of each component within our model. Furthermore, our approach outperforms other state-of-the-art methods on three benchmark datasets, demonstrating a notable advantage especially in scenarios with contaminated training data.