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A comprehensive review of generative adversarial networks: Fundamentals, applications, and challenges
In machine learning, a generative model is responsible for generating new samples of data
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
in terms of a probabilistic model. Generative adversarial network (GAN) has been widely …
On data augmentation for GAN training
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
Global evolution of research in artificial intelligence in health and medicine: a bibliometric study
The increasing application of Artificial Intelligence (AI) in health and medicine has attracted
a great deal of research interest in recent decades. This study aims to provide a global and …
a great deal of research interest in recent decades. This study aims to provide a global and …
Deep clustering by gaussian mixture variational autoencoders with graph embedding
We propose DGG: D eep clustering via a G aussian-mixture variational autoencoder (VAE)
with G raph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as …
with G raph embedding. To facilitate clustering, we apply Gaussian mixture model (GMM) as …
Infomax-gan: Improved adversarial image generation via information maximization and contrastive learning
Abstract While Generative Adversarial Networks (GANs) are fundamental to many
generative modelling applications, they suffer from numerous issues. In this work, we …
generative modelling applications, they suffer from numerous issues. In this work, we …
LEGAN: Addressing intra-class imbalance in GAN-based medical image augmentation for improved imbalanced data classification
H Ding, N Huang, Y Wu, X Cui - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Currently, the medical image classification is challenged by performance degradation due to
imbalanced data. Balancing the data through sample augmentation proves to be an effective …
imbalanced data. Balancing the data through sample augmentation proves to be an effective …
Self-supervised gan: Analysis and improvement with multi-class minimax game
Self-supervised (SS) learning is a powerful approach for representation learning using
unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) …
unlabeled data. Recently, it has been applied to Generative Adversarial Networks (GAN) …
[PDF][PDF] Towards good practices for data augmentation in gan training
Recent successes in Generative Adversarial Networks (GAN) have affirmed the importance
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
of using more data in GAN training. Yet it is expensive to collect data in many domains such …
[HTML][HTML] Cyclic consistent image style transformation: from model to system
J Peng, K Chen, Y Gong, T Zhang, B Su - Applied Sciences, 2024 - mdpi.com
Generative Adversarial Networks (GANs) have achieved remarkable success in various
tasks, including image generation, editing, and reconstruction, as well as in unsupervised …
tasks, including image generation, editing, and reconstruction, as well as in unsupervised …
DFSGAN: Introducing editable and representative attributes for few-shot image generation
Training generative adversarial networks (GANs) usually requires large-scale data and
massive computation resources. The performance of GANs plummets when given limited …
massive computation resources. The performance of GANs plummets when given limited …