Abstract: In many outlier detection tasks, only training data
belonging to one class, i.e., the positive class, is available. The
task is then to predict a new data point as belonging either to
the positive class or to the negative class, in which case the
data point is considered an outlier. For this task, we propose a
novel corrupted Generative Adversarial Network (CorGAN). In the
adversarial process of training CorGAN, the Generator generates
outlier samples for the negative class, and the Discriminator is trained
to distinguish the positive training data from the generated negative
data. The proposed framework is evaluated using an image dataset
and a real-world network intrusion dataset. Our outlier-detection
method achieves state-of-the-art performance on both tasks.