secGAN

Wu, Hao; Feng, Jinghao; Tian, Xuejin; Xu, Fengyuan; Liu, Yunxin; Sheng, Zhaohan

doi:10.1145/3349614.3356024

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…Hence, Arjovsky et al (2017) conducted a theoretical analysis of the generative adversarial network and provided an optimal solution. Later, new results appeared in the field of image processing, such as Style GAN ( Karras et al, 2019 ), Cycle GAN ( Zhu et al, 2017 ), SeCGAN ( Wu et al, 2019 ), etc. In recent years, many researchers have begun to explore the application of generative adversarial networks in other fields.…”

Section: Introductionmentioning

confidence: 99%

MADGAN:A microbe-disease association prediction model based on generative adversarial networks

Yang²,

Wang³

et al. 2023

Front. Microbiol.

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Researches have demonstrated that microorganisms are indispensable for the nutrition transportation, growth and development of human bodies, and disorder and imbalance of microbiota may lead to the occurrence of diseases. Therefore, it is crucial to study relationships between microbes and diseases. In this manuscript, we proposed a novel prediction model named MADGAN to infer potential microbe-disease associations by combining biological information of microbes and diseases with the generative adversarial networks. To our knowledge, it is the first attempt to use the generative adversarial network to complete this important task. In MADGAN, we firstly constructed different features for microbes and diseases based on multiple similarity metrics. And then, we further adopted graph convolution neural network (GCN) to derive different features for microbes and diseases automatically. Finally, we trained MADGAN to identify latent microbe-disease associations by games between the generation network and the decision network. Especially, in order to prevent over-smoothing during the model training process, we introduced the cross-level weight distribution structure to enhance the depth of the network based on the idea of residual network. Moreover, in order to validate the performance of MADGAN, we conducted comprehensive experiments and case studies based on databases of HMDAD and Disbiome respectively, and experimental results demonstrated that MADGAN not only achieved satisfactory prediction performances, but also outperformed existing state-of-the-art prediction models.

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Section: Introductionmentioning

confidence: 99%

MADGAN:A microbe-disease association prediction model based on generative adversarial networks

Yang²,

Wang³

et al. 2023

Front. Microbiol.

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Dynamic GAN for high-quality sign language video generation from skeletal poses using generative adversarial networks

Natarajan

Elakkiya

2022

Soft Comput

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The emergence of unsupervised generative models has resulted in greater performance in image and video generation tasks. However, existing generative models pose huge challenges in high-quality video generation process due to blurry and inconsistent results. In this paper, we introduce a novel generative framework named Dynamic Generative Adversarial Networks (Dynamic GAN) model for regulating the adversarial training and generating photorealistic high-quality sign language videos from skeletal poses. The proposed model comprises three stages of development such as generator network, classification and image quality enhancement and discriminator network. In the generator fold, the model generates samples similar to real images using random noise vectors, the classification of generated samples are carried out using the VGG-19 model and novel techniques are employed for improving the quality of generated samples in the second fold of the model and finally the discriminator networks fold identifies the real or fake samples. Unlike, existing approaches the proposed novel framework produces photo-realistic video quality results without using any animation or avatar approaches. To evaluate the model performance qualitatively and quantitatively, the proposed model has been evaluated using three benchmark datasets that yield plausible results. The datasets are RWTH-PHOENIX-Weather 2014T dataset, and our self-created dataset for Indian Sign Language (ISL-CSLTR), and the UCF-101 Action Recognition dataset. The output samples and performance metrics show the outstanding performance of our model.

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INSPIRE: Instance-Level Privacy-Pre Serving Transformation for Vehicular Camera Videos

Li,

Yu,

Das

et al. 2023

2023 32nd International Conference on Computer Communications and Networks (ICCCN)

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secGAN

Cited by 7 publications

References 11 publications

MADGAN:A microbe-disease association prediction model based on generative adversarial networks

MADGAN:A microbe-disease association prediction model based on generative adversarial networks

Dynamic GAN for high-quality sign language video generation from skeletal poses using generative adversarial networks

INSPIRE: Instance-Level Privacy-Pre Serving Transformation for Vehicular Camera Videos

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