Researchers have created a Generative Adversarial Network (GAN) for anomaly detection in manufacturing. By deploying a method that clusters data and individually assesses manifolds for anomalies, this method significantly outperforms other GANs in testing. Within particular datasets, D-AnoGAN can be trained to detect industry-specific anomalies with a very low error rate.