In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real. The generator is trained to increase the probability that fake data is real. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Generation||CAT 256x256||RaSGAN||FID||32.11||# 1|
|Image Generation||CIFAR-10||RSGAN-GP||FID||25.60||# 19|