  The training of Generative Adversarial Networks is a difficult task mainly
due to the nature of the networks. One such issue is when the generator and
discriminator start oscillating, rather than converging to a fixed point.
Another case can be when one agent becomes more adept than the other which
results in the decrease of the other agent's ability to learn, reducing the
learning capacity of the system as a whole. Additionally, there exists the
problem of Mode Collapse which involves the generators output collapsing to a
single sample or a small set of similar samples. To train GANs a careful
selection of the architecture that is used along with a variety of other
methods to improve training. Even when applying these methods there is low
stability of training in relation to the parameters that are chosen. Stochastic
ensembling is suggested as a method for improving the stability while training
GANs.
