GAN-based Anomaly Detection in Imbalance Problems

Imbalance problems in object detection are one of the key issues that affect the performance greatly. Our focus in this work is to address an imbalance problem arising from defect detection in industrial inspections, including the different number of defect and non-defect dataset, the gap of distribution among defect classes, and various sizes of defects. To this end, we adopt the anomaly detection method that is to identify unusual patterns to address such challenging problems. Especially generative adversarial network (GAN) and autoencoder-based approaches have shown to be effective in this field. In this work, 1) we propose a novel GAN-based anomaly detection model which consists of an autoencoder as the generator and two separate discriminators for each of normal and anomaly input; and 2) we also explore a way to effectively optimize our model by proposing new loss functions: Patch loss and Anomaly adversarial loss, and further combining them to jointly train the model. In our experiment, we evaluate our model on conventional benchmark datasets such as MNIST, Fashion MNIST, CIFAR 10/100 data as well as on real-world industrial dataset – smartphone case defects. Finally, experimental results demonstrate the effectiveness of our approach by showing the results of outperforming the current State-OfThe-Art approaches in terms of the average area under the ROC curve (AUROC).