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 …
A survey on generative adversarial networks for imbalance problems in computer vision tasks
Any computer vision application development starts off by acquiring images and data, then
preprocessing and pattern recognition steps to perform a task. When the acquired images …
preprocessing and pattern recognition steps to perform a task. When the acquired images …
Image synthesis with adversarial networks: A comprehensive survey and case studies
Abstract Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural language …
various application domains such as computer vision, medicine, and natural language …
Recent advances of generative adversarial networks in computer vision
YJ Cao, LL Jia, YX Chen, N Lin, C Yang, B Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
The appearance of generative adversarial networks (GAN) provides a new approach and
framework for computer vision. Compared with traditional machine learning algorithms, GAN …
framework for computer vision. Compared with traditional machine learning algorithms, GAN …
Brain tumor classification using a combination of variational autoencoders and generative adversarial networks
Brain tumors are a pernicious cancer with one of the lowest five-year survival rates.
Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain …
Neurologists often use magnetic resonance imaging (MRI) to diagnose the type of brain …
GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images
Brain tumors are one of the major common causes of cancer-related death, worldwide.
Growth prediction of these tumors, particularly gliomas which are the most dominant type …
Growth prediction of these tumors, particularly gliomas which are the most dominant type …
Physics-Guided Generative Adversarial Networks for fault detection of underwater thruster
In this paper, we propose a novel hybrid framework for fault detection in underwater
thrusters based on the combination of a physical model and a Generative Adversarial …
thrusters based on the combination of a physical model and a Generative Adversarial …
Skin lesion synthesis and classification using an improved DCGAN classifier
The prognosis for patients with skin cancer improves with regular screening and checkups.
Unfortunately, many people with skin cancer do not receive a diagnosis until the disease …
Unfortunately, many people with skin cancer do not receive a diagnosis until the disease …
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 …
Improving skin cancer classification using heavy-tailed Student t-distribution in generative adversarial networks (TED-GAN)
Deep learning has gained immense attention from researchers in medicine, especially in
medical imaging. The main bottleneck is the unavailability of sufficiently large medical …
medical imaging. The main bottleneck is the unavailability of sufficiently large medical …