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Generative adversarial networks

Generative adversarial networks are a neural network framework where a generative model is estimated via an adversarial process. Initially developed… Expand
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Papers overview

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Highly Cited
2019
Highly Cited
2019
We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature… Expand
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Highly Cited
2019
Highly Cited
2019
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range… Expand
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Highly Cited
2018
Highly Cited
2018
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we… Expand
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Highly Cited
2017
Highly Cited
2017
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the… Expand
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Highly Cited
2017
Highly Cited
2017
We introduce a new algorithm named WGAN, an alternative to traditional GAN training. In this new model, we show that we can… Expand
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Highly Cited
2017
Highly Cited
2017
While humans easily recognize relations between data from different domains without any supervision, learning to automatically… Expand
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Highly Cited
2016
Highly Cited
2016
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications… Expand
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Highly Cited
2016
Highly Cited
2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast… Expand
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Highly Cited
2016
Highly Cited
2016
This paper describes InfoGAN, an information-theoretic extension to the Generative Adversarial Network that is able to learn… Expand
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Highly Cited
2016
Highly Cited
2016
Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this… Expand
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