Fig. 5

A schematic view of variants of GANs and VAE. a The primary idea of the DCGAN compared to vanilla GAN is that adds transposed convolutional layers between the input vector Z and the output image in the generator. In addition, the discriminator incorporates convolutional layers to classify the generated and real images with the corresponding label real or synthetic. b Training a GAN is not trivial. Such models may never converge and issues such as model collapses and vanishing of gradients are common. WGAN proposes a new cost function using Wasserstein distance that has a smoother gradient. The discriminator is referred to as the critic who returns a value in a range, instead of 0 or 1, and therefore acts less strictly. c The training in PGGAN starts with a single convolution block in both generator and discriminator leading to 4 x 4 synthetic images. Real images are downsampled also to be of size 4 x 4. After a few iterations, another layer of convolution is introduced in both networks until desired resolution (e.g., 256 x 256 in the schematic). By progressively growing the network learns high-level structures first followed by finer-scale details available at higher resolutions. d In contrast to traditional autoencoders, VAE is both probabilistic and generative. The encoder learns the mean codings, \(\mu\), and standard deviation codings, \(\sigma\). Therefore the model is capable of randomly sample from a Gaussian distribution and generating the latent variables Z. These latent variables are then “decoded” to reconstruct the input