A review on generative adversarial networks: Algorithms, theory, and applications
Generative adversarial networks (GANs) have recently become a hot research topic;
however, they have been studied since 2014, and a large number of algorithms have been …
however, they have been studied since 2014, and a large number of algorithms have been …
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 …
Bagan: Data augmentation with balancing gan
Image classification datasets are often imbalanced, characteristic that negatively affects the
accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as …
accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN) as …
StudioGAN: a taxonomy and benchmark of GANs for image synthesis
Generative Adversarial Network (GAN) is one of the state-of-the-art generative models for
realistic image synthesis. While training and evaluating GAN becomes increasingly …
realistic image synthesis. While training and evaluating GAN becomes increasingly …
Reverse engineering of generative models: Inferring model hyperparameters from generated images
State-of-the-art (SOTA) Generative Models (GMs) can synthesize photo-realistic images that
are hard for humans to distinguish from genuine photos. Identifying and understanding …
are hard for humans to distinguish from genuine photos. Identifying and understanding …
[HTML][HTML] The effect of loss function on conditional generative adversarial networks
Abstract Conditional Generative Adversarial Network (cGAN) is a general purpose approach
for many image-to-image translation tasks, which aims to translate images from one form to …
for many image-to-image translation tasks, which aims to translate images from one form to …
On the effects of batch and weight normalization in generative adversarial networks
Generative adversarial networks (GANs) are highly effective unsupervised learning
frameworks that can generate very sharp data, even for data such as images with complex …
frameworks that can generate very sharp data, even for data such as images with complex …
Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling
M Asadi, KN McPhedran - Science of the Total Environment, 2021 - Elsevier
Greenhouse gas (GHG) emissions including carbon dioxide (CO 2), methane (CH 4), and
nitrous oxide (N 2 O) created via wastewater treatment processes are not easily modeled …
nitrous oxide (N 2 O) created via wastewater treatment processes are not easily modeled …
生成对抗网络研究综述
王正龙, 张保稳 - 网络与信息安全学报, 2021 - infocomm-journal.com
首先介绍了生成对抗网络基本理论, 应用场景和研究现状, 并列举了其亟待改进的问题.
围绕针对提升模型训练效率, 提升生成样本质量和降低模式崩溃现象发生可能性3 类问题的解决 …
围绕针对提升模型训练效率, 提升生成样本质量和降低模式崩溃现象发生可能性3 类问题的解决 …
Diff-MSR: A Diffusion Model Enhanced Paradigm for Cold-Start Multi-Scenario Recommendation
With the explosive growth of various commercial scenarios, there is an increasing number of
studies on multi-scenario recommendation (MSR) which trains the recommender system …
studies on multi-scenario recommendation (MSR) which trains the recommender system …