Unsupervised Anomaly Detection via Generative Adversarial Networks

Unsupervised anomaly detection is a fundamental problem in various research areas and application domains, namely the discrimination of abnormal samples from normal samples where training data are only composed of one class (normal) while testing data contains both among which the majority are normal samples. However, previous works can not effectively fit the distribution of high dimensional data and suffers from low AUC scores which measures the classification performance of imbalanced data. To solve these problems, we propose an unsupervised anomaly detection model based on GAN, i.e., UAD-GAN. Specifically, we adopt transfer learning to extract visual features with pre-trained Inception-v3 model and use the discriminator to detect anomalies. UAD-GAN can fit the data distribution and detect anomalies efficiently. Extensive experiments show that UAD-GAN achieves state-of-the-art performance compared to other approaches.