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Improved Techniques for Training GANs

Tim Salimans · Ian Goodfellow · Wojciech Zaremba · Vicki Cheung · Alec Radford · Peter Chen · Xi Chen

Area 5+6+7+8 #166

Keywords: [ Semi-Supervised Learning ] [ Deep Learning or Neural Networks ]


We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework. Using our new techniques, we achieve state-of-the-art results in semi-supervised classification on MNIST, CIFAR-10 and SVHN. The generated images are of high quality as confirmed by a visual Turing test: Our model generates MNIST samples that humans cannot distinguish from real data, and CIFAR-10 samples that yield a human error rate of 21.3%. We also present ImageNet samples with unprecedented resolution and show that our methods enable the model to learn recognizable features of ImageNet classes.

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