However, a simple DCGAN doesn't let us control the appearance (e.g. class) of the samples we're generating. For instance, with a GAN that generates MNIST handwritten digits, a simple DCGAN wouldn't let us choose the class of digits we're generating. To be able to control what we generate, we need to condition the GAN output on a semantic input, such as the class of an image.