Generative adversarial networks
RETRACTED: Realization of Virtual Human Face Based on Deep Convolutional Generative Adversarial Networks
... Processing generative adversarial networks and used them for LSUN scene recognition challenges, Mnist handwritten numbers, and SVNH data sets ...convolutional generative adversarial ...
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Age Estimation Method Based on Generative Adversarial Networks
... newest generative adversarial networks to generate samples lacking of age groups to solve the problem of uneven age ...convolution networks with a feature of imitation to realize face image ...
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Latent Code and Text based Generative Adversarial Networks for Soft text Generation
... However, generative adversarial networks (GANs) (Goodfellow et ...an adversarial loss function suffers less from the mentioned problems of the MLE ...
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Compression artifacts reduction by improved generative adversarial networks
... on generative adversarial networks (GANs) ...an adversarial process alternating between identifying and faking, and the generative ad- versarial losses are formulated to evaluate the ...
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Generative Adversarial Networks: Outline and its Use Cases
... Abstract: Generative adversarial networks (GANs) are the youngest neural networks which have been in the highlight for recent years ...of Generative Adversarial Model, its ...
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Neuroevolutionary Training of Deep Convolutional Generative Adversarial Networks
... In March 2019, Costa et al. proposed coevolution of Generative Adversarial Networks (COEGAN) [12]. In COEGAN authours combines neuroevolution and coevolution in the coordination of the GAN training ...
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ClusterGAN: Latent Space Clustering in Generative Adversarial Networks
... of generative models, VAE, has the additional advantage of having an inference network, the encoder, which is jointly learnt during ...rectional Generative Adversarial Networks (BiGAN) ...
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Hippocampal subfields segmentation in brain MR images using generative adversarial networks
... on generative adversarial networks was proposed to achieve the higher accurate hippocampal subfields ...an adversarial model and training the model against each other alter- nately, we ...
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Large-Scale Distributed Training Applied to Generative Adversarial Networks for Calorimeter Simulation
... particular, generative adversarial networks are a good can- didate to supplement the simulation of the detector response in a collider envi- ...training generative adversarial ...
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Audio Enhancement and Synthesis using Generative Adversarial Networks: A Survey
... Generative adversarial networks (GAN) are a recent introduction to supervised and unsupervised machine ...two networks compete until the optimal solution is ...from generative models ...
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Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes
... This thesis attempts to use Generative Adversarial Networks (GANs) to enable the fast classification of rail and road damages caused by earthquakes. Earthquakes can cause tremendous damage to ...
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Generative adversarial networks for augmenting training data of microscopic cell images
... Generative adversarial networks (GANs) have recently been successfully used to create realistic synthetic microscopy cell images in 2D and predict intermediate cell ...
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Enhancing neural non-intrusive load monitoring with generative adversarial networks
... a Generative Adversarial Network to mitigate the described ...with Generative Adversarial Networks show the potential of the approach, albeit there is no strong evidence yet that this ...
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Retinal image synthesis from multiple-landmarks input with generative adversarial networks
... Generative adversarial networks (GANs) [10] are a family of unsupervised machine learning algorithms that have demonstrated their merits through generating synthetic images and solving image-to-image ...
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A Comparative Assessment of the Impact of Various Norms on Wasserstein Generative Adversarial Networks
... Super-resolution generative adversarial networks (SR-GAN) [35] captures the finer texture details of an image and recover photo-realistic textures as shown in one of the samples in Figure ...
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A Survey on Generative Adversarial Networks (GANs)
... Abstract:- Generative adversarial networks are one of the recent research areas in deep ...the adversarial process it follows and the two models in its architecture – the generator and the ...
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Generative Adversarial Networks: A Comparative Analysis
... The Generative Adversarial Networks (GANs) have been studied and applied to tremendous applications. The versatility of GANs have made them popular. The earlier literature studies and research ...
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A Study of Generative Adversarial Networks in 3D Modelling
... Generative Adversarial Networks are the front-runners of Deep Generative Programming, and are expected to generate new data by learning the data distribution ...
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Generative Adversarial Networks for Text Using Word2vec Intermediaries
... We observe that the performance of GAN2vec gets better with an increase in the cut-off length of the sentences. This improvement could be because of extra training points for the model. The drop from BLEU-2 to BLEU-3 ...
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Learning to Encode Text as Human Readable Summaries using Generative Adversarial Networks
... not readable by humans. Here, instead of reg- ularizing the generator output with a pre-trained language model (Miao and Blunsom, 2016), we borrow from adversarial auto-encoders (Makhzani et al., 2015) and cycle ...
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