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Abstract
Anomaly detection is a technique used to identify unusual patterns that do not conform to expected behavior such as defects or unusual situations. Various generative adversarial network (GAN) and autoencoder based methods have been suggested and shown much progress in this field. ABC[5], an existing representative supervised anomalies detection method, generates blur images for normal data, and the distribution of reconstruction errors for normal and abnormal overlaps considerably. In order to solve these problems, we proposed an improved anomaly detection method using autoencoder and GAN. The proposed method combine various loss functions of both supervised and unsupervised anomaly detection utilizing both normal and abnormal training data. We demonstrate the proposed GAN based anomaly detection by performing experiments on Fashion-MNIST, and real-world industrial dataset – metal surface defects. Compared to ABC[5], our model is superior to the previous approach in terms of average area under the ROC curve (AUROC) and distribution of the reconstruction errors for normal and anomaly data.
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