Results 51 to 60 of about 124,149 (320)
Coevolution of Generative Adversarial Networks [PDF]
, 2019 Published in EvoApplications ...
Victor Costa +2 more
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Spatial evolutionary generative adversarial networks [PDF]
Proceedings of the Genetic and Evolutionary Computation Conference, 2019 Generative adversary networks (GANs) suffer from training pathologies such as instability and mode collapse. These pathologies mainly arise from a lack of diversity in their adversarial interactions. Evolutionary generative adversarial networks apply the principles of evolutionary computation to mitigate these problems.
Toutouh, Jamal +2 more
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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.
Seyed Mehdi Iranmanesh +1 more
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A progressive growing of conditional generative adversarial networks model
Dianxin kexue, 2023 Progressive growing of generative adversarial networks (PGGAN) is an adversarial network model that can generate high-resolution images.However, when the categories of samples are unbalanced, or the categories of samples are too similar or too dissimilar,
Hui MA, Ruiqin WANG, Shuai YANG
doaj +2 more sources
Semi-supervised generative adversarial networks for anomaly detection [PDF]
SHS Web of Conferences, 2022 Advancements in security have provided ways of recording anomalies of daily life through video surveillance. For the present investigation, a semi-supervised generative adversarial network model to detect and classify different types of crimes on videos.
Montenegro Juan, Chung Yeojin
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Slimmable Generative Adversarial Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2021 Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
Hou, Liang +5 more
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EvolGAN: Evolutionary Generative Adversarial Networks [PDF]
, 2021 We propose to use a quality estimator and evolutionary methods to search the latent space of generative adversarial networks trained on small, difficult datasets, or both. The new method leads to the generation of significantly higher quality images while preserving the original generator's diversity.
Roziere, Baptiste +6 more
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Training Generative Adversarial Networks With Weights [PDF]
2019 27th European Signal Processing Conference (EUSIPCO), 2019 The impressive success of Generative Adversarial Networks (GANs) is often overshadowed by the difficulties in their training. Despite the continuous efforts and improvements, there are still open issues regarding their convergence properties. In this paper, we propose a simple training variation where suitable weights are defined and assist the ...
Pantazis, Yannis +3 more
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An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs
Applied Sciences, 2020 Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
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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
core +1 more source