Generative adversarial networks (GANs) challenges, solutions, and future directions
Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …
has recently gained significant attention. GANs learn complex and high-dimensional …
GAN computers generate arts? A survey on visual arts, music, and literary text generation using generative adversarial network
S Shahriar - Displays, 2022 - Elsevier
Abstract “Art is the lie that enables us to realize the truth.”–Pablo Picasso. For centuries,
humans have dedicated themselves to producing arts to convey their imagination. The …
humans have dedicated themselves to producing arts to convey their imagination. The …
Dermoscopy image classification based on StyleGAN and DenseNet201
C Zhao, R Shuai, L Ma, W Liu, D Hu, M Wu - Ieee Access, 2021 - ieeexplore.ieee.org
Melanoma is considered one of the most lethal skin cancers. However, skin lesion
classification based on deep learning diagnostic techniques is a challenging task owing to …
classification based on deep learning diagnostic techniques is a challenging task owing to …
Improving cervical cancer classification with imbalanced datasets combining taming transformers with T2T-ViT
C Zhao, R Shuai, L Ma, W Liu, M Wu - Multimedia tools and applications, 2022 - Springer
Cervical cell classification has important clinical significance in cervical cancer screening at
early stages. However, there are fewer public cervical cancer smear cell datasets, the …
early stages. However, there are fewer public cervical cancer smear cell datasets, the …
SAM-GAN: Self-Attention supporting Multi-stage Generative Adversarial Networks for text-to-image synthesis
D Peng, W Yang, C Liu, S Lü - Neural Networks, 2021 - Elsevier
Synthesizing photo-realistic images based on text descriptions is a challenging task in the
field of computer vision. Although generative adversarial networks have made significant …
field of computer vision. Although generative adversarial networks have made significant …
Vibration-based fault diagnosis of the natural gas compressor using adaptive stochastic resonance realized by Generative Adversarial Networks
D Zhou, D Huang, J Hao, Y Ren, P Jiang… - Engineering Failure …, 2020 - Elsevier
The compressor as an important energy transmission equipment is widely used in the
natural gas pipelines to pressurize natural gas, which is prone to fail due to the …
natural gas pipelines to pressurize natural gas, which is prone to fail due to the …
Using conditional generative adversarial 3-D convolutional neural network for precise radar extrapolation
C Wang, P Wang, P Wang, B Xue… - IEEE Journal of Selected …, 2021 - ieeexplore.ieee.org
Radar echo extrapolation is a basic but essential task in meteorological services. It could
provide radar echo prediction results with high spatiotemporal resolution in a …
provide radar echo prediction results with high spatiotemporal resolution in a …
Games of GANs: Game-theoretical models for generative adversarial networks
Abstract Generative Adversarial Networks (GANs) have recently attracted considerable
attention in the AI community due to their ability to generate high-quality data of significant …
attention in the AI community due to their ability to generate high-quality data of significant …
Mw-acgan: Generating multiscale high-resolution SAR images for ship detection
In high-resolution Synthetic Aperture Radar (SAR) ship detection, the number of SAR
samples seriously affects the performance of the algorithms based on deep learning. In this …
samples seriously affects the performance of the algorithms based on deep learning. In this …
EAGAN: Efficient two-stage evolutionary architecture search for GANs
Generative adversarial networks (GANs) have proven successful in image generation tasks.
However, GAN training is inherently unstable. Although many works try to stabilize it by …
However, GAN training is inherently unstable. Although many works try to stabilize it by …