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In a bit more detail, the main network you want to train on the GAN is called a generator, you will learn to receive random noise vectors and convert them to realistic images. This network has a "reverse" structure from the convolution neural network, which is suitably called a "deconvolution" architecture. Another network that attempts to distinguish between genuine images and fake images is a convolution network that is exactly the same as the network used for image classification, called a "discriminator".

As mentioned in the introductory section, the most common use of the current GAN is to generate images using a convolution neural network. The following is an example of image generation GAN. The generator takes a vector input (z) and generates a 64 x 64 x 3 color image. Next, the discriminator takes the real image (x) and the generated image (G (z)) and generates their probability P (x). If the network is trained and you want to generate new images from it, you only need to call G (z) for a new set of random z vectors.

The authors introduce a new approach to embed images in potential vectors. As shown in the middle of the figure, after implementing the generator G in the first step, apply the image encoder E to minimize the mean square error (MSE) between the input potential vector and the output potential vector. In the two steps you can convert the image using Trained E and Trained G, as shown in the right column of Figure 2. Given the input image X to be transformed, use X_real from the trained image encoder E. Domain / class tag c is embedded, then Z is combined with another domain / class tag c = 2 to become input of training generator G, and as a final result it generates image X_fake.

SVG is best for vector images. If the raster image is composed of pixels, mathematics includes a vector image. When enlarging a raster image, many colored rectangles are displayed, but vector images can be enlarged or reduced indefinitely. Because mathematics that describe its shape and line work with arbitrary sizes.