Generative models trained in an unsupervised manner may set high likelihood\nand low reconstruction loss to Out-of-Distribution (OoD) samples. This\nincreases Type II errors and leads to missed anomalies, overall decreasing\nAnomaly Detection (AD) performance. In addition, AD models underperform due to\nthe rarity of anomalies. To address these limitations, we propose the OoD\nMinimum Anomaly Score GAN (OMASGAN). OMASGAN generates, in a negative data\naugmentation manner, anomalous samples on the estimated distribution boundary.\nThese samples are then used to refine an AD model, leading to more accurate\nestimation of the underlying data distribution including multimodal supports\nwith disconnected modes. OMASGAN performs retraining by including the abnormal\nminimum-anomaly-score OoD samples generated on the distribution boundary in a\nself-supervised learning manner. For inference, for AD, we devise a\ndiscriminator which is trained with negative and positive samples either\ngenerated (negative or positive) or real (only positive). OMASGAN addresses the\nrarity of anomalies by generating strong and adversarial OoD samples on the\ndistribution boundary using only normal class data, effectively addressing mode\ncollapse. A key characteristic of our model is that it uses any f-divergence\ndistribution metric in its variational representation, not requiring\ninvertibility. OMASGAN does not use feature engineering and makes no\nassumptions about the data distribution. The evaluation of OMASGAN on image\ndata using the leave-one-out methodology shows that it achieves an improvement\nof at least 0.24 and 0.07 points in AUROC on average on the MNIST and CIFAR-10\ndatasets, respectively, over other benchmark and state-of-the-art models for\nAD.\n