A Review: Generative Adversarial Networks

Gonog, Liang; Zhou, Yimin

doi:10.1109/iciea.2019.8833686

Cited by 112 publications

(82 citation statements)

References 8 publications

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“…The discriminator tries to distinguish between real data and fake (artificially generated) data generated by the generator network as shown in Figure 2. The mission GANs models that generator network is to try fooling the discriminator network and the discriminator network tries to fight from being fooled [24][25][26][27]. Figure 2.…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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The coronavirus pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

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Section: Generative Adversarial Networkmentioning

confidence: 99%

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

2020

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“…Eval. Metrics Simulation CV NET Others Pan et al [33] ✓ ✓ ✓ ✓ Turhan and Bilge [34] ✓ ✓ Cao et al [29] ✓ ✓ ✓ ✓ ✓ Goodfellow [35] ✓ ✓ Gonog and Zhou [36] ✓ ✓ ✓ ✓ Zhang et al [37] ✓ ✓ Wu et al [38] ✓ ✓ Wang et al [32] ✓ ✓ ✓ ✓ Shorten and Khoshgoftaar [39] ✓ ✓ ✓ Esfahani and Latifi [40] ✓ ✓ Creswell et al [41] ✓ ✓ ✓ Di Mattia et al [19] ✓…”

Section: Existing Publicationsmentioning

confidence: 99%

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

et al. 2021

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“…The generator takes a random noise vector z (following a Gaussian distribution) as input and outputs a generated sample G(z) without any access to real samples. The discriminator takes both a real sample P data and a generated sample P g as input and predicts the probability of D(x) or D(G(x)) [39,52], as shown in Figure 1.…”

Section: Fully Connected Ganmentioning

confidence: 99%

“…WGAN uses the Wasserstein distance instead of that of Jensen-Shannan to measure the similarity between the real data distribution and generated data distribution. The Wasserstein distance can be used to measure the distance between probability distributions P data (x) and P g (x) even if there is no overlap, where (P data , P g ) denotes the set of all joint distributions between the real distribution and the generated distribution [34,47,52]. WGAN applies a weight clipping to enforce the Lipschitz constraint on the discriminator.…”

Section: Modelmentioning

confidence: 99%

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

Alotaibi

2020

Symmetry

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Many image processing, computer graphics, and computer vision problems can be treated as image-to-image translation tasks. Such translation entails learning to map one visual representation of a given input to another representation. Image-to-image translation with generative adversarial networks (GANs) has been intensively studied and applied to various tasks, such as multimodal image-to-image translation, super-resolution translation, object transfiguration-related translation, etc. However, image-to-image translation techniques suffer from some problems, such as mode collapse, instability, and a lack of diversity. This article provides a comprehensive overview of image-to-image translation based on GAN algorithms and its variants. It also discusses and analyzes current state-of-the-art image-to-image translation techniques that are based on multimodal and multidomain representations. Finally, open issues and future research directions utilizing reinforcement learning and three-dimensional (3D) modal translation are summarized and discussed.

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A Review: Generative Adversarial Networks

Cited by 112 publications

References 8 publications

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Within the Lack of Chest COVID-19 X-ray Dataset: A Novel Detection Model Based on GAN and Deep Transfer Learning

Generative Adversarial Networks (GANs) in networking: A comprehensive survey & evaluation

Deep Generative Adversarial Networks for Image-to-Image Translation: A Review

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