Peptides designed by artificial intelligence restrict both drug-resistant bacteria and rapidly evolving viruses.

GANs consist of two neural networks - a generator and a discriminator - engaged in a continuous learning ... Additionally, the study introduced GAN-Econ, a novel GAN-based architecture tailored for ...

This paper addresses the aforementioned issue by introducing a GAN framework that provides novel adversarial training via discriminators in the wavelet and spatial domains. The wavelet-domain ...

Figure 1. The architecture of the original GAN. Conversely, if the input is synthetic data, the discriminator should classify it as false data and return a value close to 0. The false signal output ...

Abstract: We propose a method for recognizing continuous indoor daily human activities among continuous indoor IoT (Internet of Things) smart environments using millimeter wave radar. Focusing on the ...

GaN power devices have gained prominence in medium- and high-power applications due to their ability to operate at high frequencies while maintaining excellent efficiency. Deliver higher efficiency, ...

Official Repository for the paper "Generative Modeling for Interpretable Failure Detection in Liver CT Segmentation and Scalable Data Curation of Chest Radiographs " ...

I have tried this in the past, when GANs were still dominant. But at the time I was either too inexperienced or the research not there. Either way could not get it working. Will give it another shot ...

The Discriminator is trained with discriminator adversarial loss only. Generator and Discriminator follow the architecture of Wasserstein GAN with gradient penalty (WGAN-GP) for more stable learning.