Generating, With Style: The Mechanics Behind NVIDIA's Highly Realistic GAN Images

Although the z vector is just sampled randomly, our ultimate goal is to create a mapping between the distribution of images and our reference distribution Z, such that each vector in z corresponds to a plausibly real image. As a result, despite being meaningless at first, each particular z ends up corresponding to and encoding properties of the image that it will produce. In their simplest form, transposed convolutions work by learning a filter matrix (for example, 3x3), and multiplying that by the value at each pixel to expand its information outward spatially. Each of the single "pixels" in a 4x4 representation influences the values in a 3x3 patch of output; these patches overlap and sum to create the final "blown out" representation. The visual above, while good for building simplified intuition, is a little misleading, since it makes it look like the values of the enlarged patch have to be spun out of a single piece of information from a single pixel.