Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies
a progressive section in deep learning. Using generative modeling, the underlying …

Recently, Vision Transformers (ViTs) have shown competitive performance on image
recognition while requiring less vision-specific inductive biases. In this paper, we investigate …

Abstract Conditional Generative Adversarial Networks (cGAN) generate realistic images by
incorporating class information into GAN. While one of the most popular cGANs is an …

Generative Adversarial Networks (GANs) have been widely applied in different scenarios
thanks to the development of deep neural networks. The original GAN was proposed based …

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 …

Generative adversarial networks (GANs) represent a zero-sum game between two machine
players, a generator and a discriminator, designed to learn the distribution of data. While …

Abstract In a Distributed Denial of Service (DDoS) attack, a network of compromised devices
is used to overwhelm a target with a flood of requests, making it unable to serve legitimate …

When formulated as an unsupervised learning problem, anomaly detection often requires a
model to learn the distribution of normal data. Previous works modify Generative Adversarial …

Generative adversarial networks (GANs) represent a zero-sum game between two machine
players, a generator and a discriminator, designed to learn the distribution of data. While …

Abstract Latest Generative Adversarial Networks (GANs) are gathering outstanding results
through a large-scale training, thus employing models composed of millions of parameters …