Results 41 to 50 of about 124,149 (320)
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 networks [PDF]
Physical Review A, 2018 10 pages, 8 ...
Nathan Killoran +1 more
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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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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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GAN and Chinese WordNet Based Text Summarization Technology [PDF]
Jisuanji kexue, 2022 Since the introduction of neural networks,text summarization techniques continue to attract the attention of resear-chers.Similarly,generative adversarial networks(GANs)can be used for text summarization because they can generate text features or learn ...
LIU Xiao-ying, WANG Huai, WU Jisiguleng
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NAG: Network for Adversary Generation [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Adversarial perturbations can pose a serious threat for deploying machine learning systems. Recent works have shown existence of image-agnostic perturbations that can fool classifiers over most natural images. Existing methods present optimization approaches that solve for a fooling objective with an imperceptibility constraint to craft the ...
Utkarsh Ojha +3 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
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A quantum generative adversarial network for distributions [PDF]
Quantum Machine Intelligence, 2021 AbstractRecent advances in Quantum Computing have shown that, despite the absence of a fault-tolerant quantum computer so far, quantum techniques are providing exponential advantage over their classical counterparts. We develop a fully connected Quantum Generative Adversarial network and show how it can be applied in Mathematical Finance, with a ...
Alexei Kondratyev +3 more
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Sparse Generative Adversarial Network [PDF]
2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), 2019 We propose a new approach to Generative Adversarial Networks (GANs) to achieve an improved performance with additional robustness to its so-called and well recognized mode collapse. We first proceed by mapping the desired data onto a frame-based space for a sparse representation to lift any limitation of small support features prior to learning the ...
Hamid Krim +2 more
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Recent Generative Adversarial Approach in Face Aging and Dataset Review
IEEE Access, 2022 Many studies have been conducted in the field of face aging, from approaches that use pure image-processing algorithms, to those that use generative adversarial networks.
Hady Pranoto +3 more
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