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Generative Adversarial Networks (GANs)
ACM Computing Surveys, 2021 Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to ...
Divya Saxena, Jiannong Cao 0001
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Generative Adversarial Networks (GANs)
ACM Computing Surveys, 2022Divya Saxena, Jiannong Cao
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Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)
IEEE Transactions on Cybernetics, 2021 He C, Huang S, Cheng R, Tan KC, Jin Y. Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs). IEEE Transactions on Cybernetics.
Cheng He, Shihua Huang, Ran Cheng
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Evolutionary Generative Adversarial Networks
IEEE Transactions on Evolutionary Computation, 2019 © 1997-2012 IEEE. Generative adversarial networks (GANs) have been effective for learning generative models for real-world data. However, accompanied with the generative tasks becoming more and more challenging, existing GANs (GAN and its variants) tend ...
Chaoyue Wang, Chang Xu, Xin Yao
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Poly-GAN: Regularizing Polygons with Generative Adversarial Networks [PDF]
, 2023 Regularizing polygons involves simplifying irregular and noisy shapes of built environment objects (e.g. buildings) to ensure that they are accurately represented using a minimum number of vertices. It is a vital processing step when creating/transmitting online digital maps so that they occupy minimal storage space and bandwidth. This paper presents a
Lasith Niroshan, James D. Carswell
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Generative Adversarial Network (GAN) for Simulating Electroencephalography
Brain Topography, 2023Electroencephalographs record the electrical activity of your brain through the scalp. Electroencephalography is difficult to obtain due to its sensitivity and variability. Applications of electroencephalography such as for diagnosis, education, brain-computer interfaces require large samples of electroencephalography recording, however, it is often ...
Priyanshu, Mahey +3 more
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Applications of generative adversarial networks (GANs) in radiotherapy: narrative review
Precision Cancer Medicine, 2022Background and Objective: Radiation therapy (RT) is the dominant method for clinical cancer treatment, which aims to ensure that planning target volume (PTV) receives a sufficient dose while organs-at-risk (OARs) are exposed to little or no radiation. However, obtaining a clinically acceptable radiotherapy plan often requires a long time, tedious work,
Wang, Zhixiang +4 more
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Generative Adversarial Networks (GANs)
2020Deep learning has launched a profound reformation and has even been applied to many real-world tasks such as image classification (He et al. 2016), object detection (Ren et al. 2015), and image segmentation (Long et al. 2015). These tasks all fall into the scope of supervised learning, which means that large amounts of labeled data are provided for the
Xudong Mao, Qing Li
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On Evaluating Video-based Generative Adversarial Networks (GANs)
2018 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2018We study the problem of evaluating video-based Generative Adversarial Networks (GANs) by applying existing image quality assessment methods to the explicit evaluation of videos generated by state-of-the-art frameworks [1]–[3]. Specifically, we provide results and discussion on using quantitative methods such as the Frechet Inception Distance [4], the ...
Nancy Ronquillo, Josh Harguess
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SP-GAN: Self-Growing and Pruning Generative Adversarial Networks
IEEE Transactions on Neural Networks and Learning Systems, 2021This article presents a new Self-growing and Pruning Generative Adversarial Network (SP-GAN) for realistic image generation. In contrast to traditional GAN models, our SP-GAN is able to dynamically adjust the size and architecture of a network in the training stage by using the proposed self-growing and pruning mechanisms. To be more specific, we first
Xiaoning Song +5 more
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