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 …

Generative Adversarial Networks (GANs) is a novel class of deep generative models that
has recently gained significant attention. GANs learn complex and high-dimensional …

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 …

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 …

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 …

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 …

Generative adversarial networks (GANs) are highly effective unsupervised learning
frameworks that can generate very sharp data, even for data such as images with complex …

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 …

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 …

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 …