the generator. Transformer GAN (TransGAN): Uses the pure transformer architecture for both the generator and discriminator, entirely devoid of convolution-deconvolution layers. Flow-GAN: Uses flow-based generative model for the generator, allowing efficient computation of the likelihood function. GANs with alternative objectives Many GAN variants are merely obtained by changing the loss functions for the generator and discriminator. Original GAN: We recast the original GAN objective into a form more convenient for comparison: Original GAN, non-saturating loss: This objective for generator was recommended in the original paper for faster convergence.The effect of using this objective is analyzed in Section 2.2.2 of Arjovsky et al. Original GAN, maximum likelihood: where is the logistic function. When the discriminator is optimal, the generator gradient is the same as in maximum likelihood estimation, even though GAN cannot perform maximum likelihood estimation itself. Hinge loss GAN:Least squares GAN:where are parameters to be chosen. The authors recommended . Wasserstein GAN (WGAN) The Wasserstein GAN modifies the GAN game at two points: The discriminator's strategy set is the set of measurable functions of type with bounded Lipschitz norm: , where is a fixed positive constant. The objective is One of its purposes is to solve the problem of mode collapse (see above). The authors claim "In no experiment did we see evidence of mode collapse for the WGAN algorithm". GANs with more than 2 players Adversarial autoencoder An adversarial autoencoder (AAE) is more autoencoder than GAN. The idea is to start with a plain autoencoder, but train a discriminator to discriminate the latent vectors from a reference distribution (often the normal distribution). InfoGAN In conditional GAN, the generator receives both a noise vector and a label , and produces an image . The discriminator receives image-label pairs , and computes . When the training dataset is unlabeled, conditional GAN does not work directly. The idea of InfoGAN is to decree that every latent vector in the latent space can be decomposed as : an incompressible noise part , and an informative label part , and encourage the generator to comply with the decree, by encouraging it to maximize , the mutual information between and , while making no demands on the mutual information between . Unfortunately, is intractable in general, The key idea of InfoGAN is Variational Mutual Information Maximization: indirectly maximize it by maximizing a lower boundwhere ranges over all Markov kernels of type . The InfoGAN game