Improved Anomaly Detection Using Efficient GAN and Mapping Consistency

Anomaly detection is essential in a wide range of fields. In this study, we focus on an Efficient GAN applied to anomaly detection, and aim to improve its performance by random erasing data augmentation and enhancing the loss function to incorporate mapping consistency. Experiments using images of normal lemons and damaged lemons reveal that the proposed method significantly improves the anomaly detection performance of Efficient GAN.