To rectify the issue mentioned in the previous section, researchers have implemented a technique that takes advantage of the Earth Mover's Distance (EMD). EMD is a method of understanding the dissimilarity between two multidimensional sets of data samples. In our case, the two data sets are the real CrysTens' constructed from PCD and the samples produced by the generator. The theoretical "distance" between the two sets of data samples can be used to construct a loss function called the Wasserstein Loss, and the GANs that take advantage of this are known as Wasserstein GANs (WGANs). Using Wasserstein Loss, WGANs no longer need to predict if a sample is real or fake based on a probability between 0 and 1. Rather, the mathematics of the EMD equation allows WGANs to instead predict the "realness" of a sample. The discriminator is replaced with a critic function by changing the last layer from a sigmoid activation function to a linear activation function. This adjustment to the training scheme of the GAN rectifies the problem discussed earlier because the linear activation function has the same slope no matter what x-value is used.