Generative Adversarial Networks have two models, a Generator model G(z) and a Discriminator model D(x), in competition with each other. G tries to estimate the distribution of the training data and D tries to estimate the probability that a data sample came from the original training data and not from G. During training, the Generator learns a mapping from a prior distribution p(z) to the data space G(z). The discriminator D(x) produces a probability value of a given x coming from the actual training data. This model can be modified to include additional inputs, y, on which the models can be conditioned. y can be any type of additional inputs, for example, class labels. The conditioning can be achieved by simply feeding y to both the Generator — G(z|y) and the Discriminator — D(x|y).
cd vision/cdcgan_mnist
julia --project cGAN_mnist.jl1000 training steps
3000 training steps
5000 training steps
10000 training steps
11725 training steps
