Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN

arXiv:2304.07769v3 Announce Type: replace
Abstract: This study presents an adversarial method for anomaly detection in real-world applications, leveraging the power of generative adversarial neural networks (GANs) through cycle consistency in reconstruction error. Previous methods suffer from the high variance between class-wise accuracy which leads to not being applicable for all types of anomalies. The proposed method named RCALAD tries to solve this problem by introducing a novel discriminator to the structure, which results in a more efficient training process. Additionally, …

anomaly arxiv consistent cs.lg gan spot type