  In this paper, we propose Orthogonal Generative Adversarial Networks
(O-GANs). We decompose the network of discriminator orthogonally and add an
extra loss into the objective of common GANs, which can enforce discriminator
become an effective encoder. The same extra loss can be embedded into any kind
of GANs and there is almost no increase in computation. Furthermore, we discuss
the principle of our method, which is relative to the fully-exploiting of the
remaining degrees of freedom of discriminator. As we know, our solution is the
simplest approach to train a generative adversarial network with auto-encoding
ability.
