Generative Adversarial, Computer Vision, Gan, Machine Learning, Computer Science.
the Wasserstein distance as the objective function. In GANs, the objective function is the Jenson-Shannon divergence, which measures the difference between the real data distribution and the generated data distribution. However, this objective function can be difficult to optimize and can lead to