  Wasserstein-GANs have been introduced to address the deficiencies of
generative adversarial networks (GANs) regarding the problems of vanishing
gradients and mode collapse during the training, leading to improved
convergence behaviour and improved image quality. However, Wasserstein-GANs
require the discriminator to be Lipschitz continuous. In current
state-of-the-art Wasserstein-GANs this constraint is enforced via gradient norm
regularization. In this paper, we demonstrate that this regularization does not
encourage a broad distribution of spectral-values in the discriminator weights,
hence resulting in less fidelity in the learned distribution. We therefore
investigate the possibility of substituting this Lipschitz constraint with an
orthogonality constraint on the weight matrices. We compare three different
weight orthogonalization techniques with regards to their convergence
properties, their ability to ensure the Lipschitz condition and the achieved
quality of the learned distribution. In addition, we provide a comparison to
Wasserstein-GANs trained with current state-of-the-art methods, where we
demonstrate the potential of solely using orthogonality-based regularization.
In this context, we propose an improved training procedure for Wasserstein-GANs
which utilizes orthogonalization to further increase its generalization
capability. Finally, we provide a novel metric to evaluate the generalization
capabilities of the discriminators of different Wasserstein-GANs.
