We can enjoy the best of both worlds by incorporating the merits of both approaches: First the WGAN approach locates a good neighborhood of the parameter space, and then, starting from a reasonable initialization, the EMDOT approach efficiently explores the neighborhood to achieve enhanced performance.
the use of Wasserstein GAN addresses this problem and allows our simple architecture to be trained successfully.