Improved Techniques for Training GANs

NeurIPS 2016 Tim Salimans • Ian Goodfellow • Wojciech Zaremba • Vicki Cheung • Alec Radford • Xi Chen

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.

Full paper

Evaluation


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