Deep Learning, Machine Learning, Neural Networks, Computer Vision, Artificial Intelligence.
model. 4. Wasserstein GAN Loss Introduced by Martin Arjovsky, et al. (2017). They observed that the traditional GAN is motivated to minimize the distance between the actual and predicted probability distributions for real and generated images, the so-called Kullback-Leibler (KL) divergence.