Generative adversarial networks

Generative adversarial networks are a neural network framework where a generative model is estimated via an adversarial process. Initially developed… (More)
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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… (More)
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Highly Cited
2018
Highly Cited
2018
In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range… (More)
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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… (More)
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Highly Cited
2017
Highly Cited
2017
Although Generative Adversarial Networks achieve state-of-the-art results on a variety of generative tasks, they are regarded as… (More)
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Highly Cited
2017
Highly Cited
2017
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder… (More)
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Highly Cited
2017
Highly Cited
2017
The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully… (More)
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Highly Cited
2017
Highly Cited
2017
As society continues to accumulate more and more data, demand for machine learning algorithms that can learn from data with… (More)
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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… (More)
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Review
2016
Review
2016
This report summarizes the tutorial presented by the author at NIPS 2016 on generative adversarial networks (GANs). The tutorial… (More)
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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… (More)
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