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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… 
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Broader (1)

Cognitive science
Generative model

Papers overview

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Highly Cited
2019
Highly Cited
2019
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Previous works (Donahue et al., 2018a; Engel et al., 2019a) have found that generating coherent raw audio waveforms with GANs is… 
Highly Cited
2017
Highly Cited
2017
Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
While humans easily recognize relations between data from different domains without any supervision, learning to automatically… 
Highly Cited
2017
Highly Cited
2017
BEGAN: Boundary Equilibrium Generative Adversarial Networks
We propose a new equilibrium enforcing method paired with a loss derived from the Wasserstein distance for training auto-encoder… 
Highly Cited
2017
Highly Cited
2017
Data Augmentation Generative Adversarial Networks
Effective training of neural networks requires much data. In the low-data regime, parameters are underdetermined, and learnt… 
Highly Cited
2017
Highly Cited
2017
Image De-Raining Using a Conditional Generative Adversarial Network
Severe weather conditions, such as rain and snow, adversely affect the visual quality of images captured under such conditions… 
Highly Cited
2017
Highly Cited
2017
SEGAN: Speech Enhancement Generative Adversarial Network
Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of… 
Highly Cited
2016
Highly Cited
2016
Coupled Generative Adversarial Networks
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast… 
Highly Cited
2016
Highly Cited
2016
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks
Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks… 
Highly Cited
2015
Highly Cited
2015
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach… 
Highly Cited
2015
Highly Cited
2015
Generative Moment Matching Networks
We consider the problem of learning deep generative models from data. We formulate a method that generates an independent sample… 
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