Improved Techniques for Training GANs

NeurIPS 2016 Tim SalimansIan GoodfellowWojciech ZarembaVicki CheungAlec RadfordXi 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... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Image Generation CIFAR-10 Improved GAN Inception score 6.86 # 10
Conditional Image Generation CIFAR-10 Improved GAN Inception score 8.09 # 5
Semi-Supervised Image Classification CIFAR-10, 4000 Labels GAN Accuracy 84.41 # 6
Semi-Supervised Image Classification SVHN, 1000 labels GAN Accuracy 91.89 # 5