Improved Techniques for Training GANs
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, Xi Chen
Paper appears method- or tooling-adjacent to AI workflows with partial ecosystem coverage.
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. 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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We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
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Evidence disclosure
Evidence graph: 2 refs, 1 links.
Utility signals: depth 85/100, grounding 58/100, status medium.
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Research context
1,386
Citations
0
References
Tasks
MNIST database, Computer science, Generative grammar, Focus (optics), Turing test, Variety (cybernetics), Supervised learning, Pattern recognition (psychology)
Methods
Generative model
Domains
Artificial intelligence, Machine learning, Image (mathematics), Computer Vision and Pattern Recognition
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