of the art on FID or IS. Another evaluation method is the Learned Perceptual Image Patch Similarity (LPIPS), which starts with a learned image featurizer , and finetunes it by supervised learning on a set of , where is an image, is a perturbed version of it, and is how much they differ, as reported by human subjects. The model is finetuned so that it can approximate . This finetuned model is then used to define . Other evaluation methods are reviewed in. Variants There is a veritable zoo of GAN variants. Some of the most prominent are as follows: Conditional GAN Conditional GANs are similar to standard GANs except they allow the model to conditionally generate samples based on additional information. For example, if we want to generate a cat face given a dog picture, we could use a conditional GAN The generator in a GAN game generates , a probability distribution on the probability space . This leads to the idea of a conditional GAN, where instead of generating one probability distribution on , the generator generates a different probability distribution on , for each given class label . For example, for generating images that look like ImageNet, the generator should be able to generate a picture of cat when given the class label "cat". In the original paper, the authors noted that GAN can be trivially extended to conditional GAN by providing the labels to both the generator and the discriminator. Concretely, the conditional GAN game is just the GAN game with class labels provided:where is a probability distribution over classes, is the probability distribution of real images of class , and the probability distribution of images generated by the generator when given class label . In 2017, a conditional GAN learned to generate 1000 image classes of ImageNet. GANs with alternative architectures The GAN game is a general framework and can be run with any reasonable parametrization of the generator and discriminator . In the original paper, the authors demonstrated it using multilayer perceptron networks and convolutional neural networks. Many alternative architectures have been tried. Deep convolutional GAN (DCGAN): For both generator and discriminator, uses only deep networks consisting entirely of convolution-deconvolution layers, that is, fully convolutional networks. Self-attention GAN (SAGAN): Starts with the DCGAN, then adds residually-connected standard self-attention modules to the generator and discriminator. Variational autoencoder GAN (VAEGAN): Uses a variational autoencoder (VAE) for