We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. We focus on two applications of GANs: semi-supervised learning, and the generation of images that humans find visually realistic. Unlike most work on generative models, our primary goal is not to train a model that assigns high likelihood to test data, nor do we require the model to be able to learn well without using any labels.
|Task||Dataset||Model||Metric name||Metric value||Global rank||Compare|
|Image Generation||CIFAR-10||Improved GAN||Inception score||6.86||# 8|
|Conditional Image Generation||CIFAR-10||Improved GAN||Inception score||8.09||# 5|