A I is everywhere. From the massive popularity of ChatGPT to Google cramming AI summaries at the top of its search results, ...

In their study published on the arXiv preprint server, Bryan Sangwoo Kim, Jeongsol Kim, and Jong Chul Ye broke down the process of zooming in on an image and then used an existing super-resolution ...

Unofficial implementation of the SR model proposed by Pan et al. in the paper "LPSRGAN: Generative Adversarial Networks for Super-Resolution of License Plate Image". This repository follows the ...

With the advent and mass adoption of generative AI (GenAI), super apps have the potential to become more powerful and expand the scope of use cases further. Being the founder of a super app engine ...

The seminal paper “Generative Adversarial Nets” introduced the adversarial process of GANs. Although this paper does not mention deepfakes, it was the springboard for GAN-based deepfakes.

The terahertz (THz) metamaterial sensor design is typically complex and requires substantial expertise in physics. To simplify this process, we propose a novel reverse design model based on an ...

Generative Adversarial Networks (GANs) have become a transformative force in artificial intelligence, enabling the generation of highly realistic data and images. Introduced by Ian Goodfellow in 2014, ...

Schematic for generating synthetic OCT images by progressively growing generative adversarial networks. PGGANs starts at low resolution (4 × 4-pixel images), doubling to 8 × 8, 16 × 16, and so on ...

In this study, we introduce Light-ESRGAN, a novel cellular image super-resolution reconstruction model utilizing Generative Adversarial Networks (GANs). High-resolution (HR) cellular images are ...