  As a kind of generative self-supervised learning methods, generative
adversarial nets have been widely studied in the field of anomaly detection.
However, the representation learning ability of the generator is limited since
it pays too much attention to pixel-level details, and generator is difficult
to learn abstract semantic representations from label prediction pretext tasks
as effective as discriminator. In order to improve the representation learning
ability of generator, we propose a self-supervised learning framework combining
generative methods and discriminative methods. The generator no longer learns
representation by reconstruction error, but the guidance of discriminator, and
could benefit from pretext tasks designed for discriminative methods. Our
discriminative-generative representation learning method has performance close
to discriminative methods and has a great advantage in speed. Our method used
in one-class anomaly detection task significantly outperforms several
state-of-the-arts on multiple benchmark data sets, increases the performance of
the top-performing GAN-based baseline by 6% on CIFAR-10 and 2% on MVTAD.
