Improved techniques for training gans
Advances in neural information processing systems, 2016•proceedings.neurips.cc
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 …
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 …
Abstract
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.
proceedings.neurips.cc
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