Skip to main content

Table 1 An overview of GANs variants discussed in “Architecture variants” section

From: A survey on generative adversarial networks for imbalance problems in computer vision tasks

Categories GAN Type Main Architectural Contributions to GAN
Basic GAN GAN [67] Use Multilayer perceptron in the generator and discriminator
Convolutional Based GAN DCGAN [112] Employ Convolutional and transpose-convolutional layers in the discriminator and generator respectively
PROGAN [131] Progressively grow layers of GAN as training progresses
Condition based GANs cGAN [118] Control kind of image being generated using prior information
ACGAN [119] Add a classifier loss in addition to adversarial loss to reconstruct class labels
VACGAN [120] Separate out classifier loss of ACGAN by introducing separate classifier network parallel to the discriminator
infoGAN [121] Learn disentangled latent representation by maximizing mutual information between latent vector and generated images
SCGAN [122] Learn disentangled latent representation by adding the similarity constraint on the generator
Latent representation based GANs DEGAN [116] Utilize the pretrained decoder and encoder structure from VAE to transform random Gaussian noise to distribution that contains intrinsic information of the real images
VAEGAN [115] Combine VAE and GAN
AAE [113] Impose discriminator on the latent space of the autoencoder architecture
VEEGAN [117] Add reconstruction network that reverse the action of generator network to address the problem of mode collapse
BiGAN [114] Attach encoder component to learn inverse mapping of data space to latent space
Stack of GANs LAPGAN [132] Introduce Laplacian pyramid framework for an image detail enhancement
MADGAN [135] Use multiple generators to discover diverse modes of the data distribution
D2GAN [134] Employ two discriminators to address the problem of mode collapse
CycleGAN [137] Use two generators and two discriminators to accomplish unpaired image to image translation task
CoGAN [136] Use two GANs to learn a joint distribution from two-domain images
Other variants SAGAN [141] Incorporate self-attention mechanism to model long range dependencies
GRAN [133] Recurrent generative model trained using adversarial process
SRGAN [139] Use very deep convolutional layers with residual blocks for image super resolution