  Recently, anomaly scores have been formulated using reconstruction loss of
the adversarially learned generators and/or classification loss of
discriminators. Unavailability of anomaly examples in the training data makes
optimization of such networks challenging. Attributed to the adversarial
training, performance of such models fluctuates drastically with each training
step, making it difficult to halt the training at an optimal point. In the
current study, we propose a robust anomaly detection framework that overcomes
such instability by transforming the fundamental role of the discriminator from
identifying real vs. fake data to distinguishing good vs. bad quality
reconstructions. For this purpose, we propose a method that utilizes the
current state as well as an old state of the same generator to create good and
bad quality reconstruction examples. The discriminator is trained on these
examples to detect the subtle distortions that are often present in the
reconstructions of anomalous data. In addition, we propose an efficient generic
criterion to stop the training of our model, ensuring elevated performance.
Extensive experiments performed on six datasets across multiple domains
including image and video based anomaly detection, medical diagnosis, and
network security, have demonstrated excellent performance of our approach.
