Our paper titled “D-AnoGAN: Anomaly Detection in Disconnected Data Manifolds with Generative Adversarial Networks” was accepted to IJCNN 2022! We explore the problem of unsupervised anomaly detection in disconnected data manifolds. We show that a multi-generator network can be combined with a bandit to learn to cluster data into different manifolds, leading to improved performance on several datasets. This work was led by Walker Dimon.