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HGAN: Hybrid generative adversarial network [PDF]
Journal of Intelligent & Fuzzy Systems, 2021 In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood.
Iranmanesh, Seyed Mehdi +1 more
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Building Footprint Generation Using Improved Generative Adversarial Networks [PDF]
, 2018 Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes.
Li, Qingyu, Shi, Yilei, Zhu, Xiao Xiang
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Quantum generative adversarial learning [PDF]
, 2018 Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake ...
Lloyd, Seth, Weedbrook, Christian
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Learning Universal Adversarial Perturbations with Generative Models [PDF]
, 2018 Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification.
Danezis, George, Hayes, Jamie
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Self-Sparse Generative Adversarial Networks
CAAI Artificial Intelligence Research, 2022 Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem.
Wenliang Qian +3 more
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Wasserstein Introspective Neural Networks
, 2018 We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model. WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN's ...
Fan, Fan +3 more
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Bidirectional Conditional Generative Adversarial Networks
, 2018 Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$). We propose the Bidirectional cGAN (BiCoGAN), which effectively
AbdAlmageed, Wael +3 more
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Entangling Quantum Generative Adversarial Networks
Physical Review Letters, 2022 Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of ...
Murphy Yuezhen Niu +6 more
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Generative Adversarial Networks: An Overview [PDF]
IEEE Signal Processing Magazine, 2018 Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell +5 more
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Generative Adversarial Networks: A Primer for Radiologists
RadioGraphics, 2021 Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink +5 more
openaire +5 more sources