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

In this work, we address the task of natural image generation guided by a conditioning input.
We introduce a new architecture called conditional invertible neural network (cINN). The …

Abstract We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional neural …

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 …

Reinforcement learning (RL) algorithms usually require a substantial amount of interaction
data and perform well only for specific tasks in a fixed environment. In some scenarios such …

Abstract Generative Adversarial Networks (GANs) are an innovative class of deep learning
generative model that has been popular among academics recently. GANs are able to learn …

A novel method called GAN-Poser has been explored to predict human motion in less time
given an input 3D human skeleton sequence based on a generator–discriminator …

Anomaly detection is considered as a challenging task due to its imbalanced and unlabelled
nature. To overcome this challenge, the combination of different machine learning …

As the social impact of visual recognition has been under scrutiny, several protected-
attribute balanced datasets emerged to address dataset bias in imbalanced datasets …

Abstract In recent years, Generative Adversarial Network (GAN) and its variants have gained
great popularity in both academia and industry. In this chapter, we survey different state-of …