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 …

The era of artificial neural network (ANN) began with a simplified application in many fields
and remarkable success in pattern recognition (PR) even in manufacturing industries …

Generative adversarial networks (GANs) are challenging to train stably, and a promising
remedy of injecting instance noise into the discriminator input has not been very effective in …

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

Deep learning has produced state-of-the-art results for a variety of tasks. While such
approaches for supervised learning have performed well, they assume that training and …

In standard generative adversarial network (SGAN), the discriminator estimates the
probability that the input data is real. The generator is trained to increase the probability that …

Generative adversarial network (GANs) is one of the most important research avenues in the
field of artificial intelligence, and its outstanding data generation capacity has received wide …

Semi-supervised learning (SSL) provides a powerful framework for leveraging unlabeled
data when labels are limited or expensive to obtain. SSL algorithms based on deep neural …

We investigate the training and performance of generative adversarial networks using the
Maximum Mean Discrepancy (MMD) as critic, termed MMD GANs. As our main theoretical …

Generative models, in particular generative adversarial networks (GANs), have gained
significant attention in recent years. A number of GAN variants have been proposed and …