The major shortcoming associated with the sigmoid activation function directly corresponds to the difficulty of the tasks assigned to the two neural networks. It is far easier to classify if a given sample is real or fake than it is to generate an entirely new sample. During the initial training epochs, before the generator has had any time to calculate the weights necessary for accurate sample generation, it will produce samples that are very obviously fake. When the discriminator has to decide between something that is clearly real and something that is obviously fake, it will start performing very well and will begin predicting with higher confidence (a value closer to either 1 or 0).