2022/01/23 by Soheyla Amirian, Amirian, Soheyla, Thiab R. Taha +5
Computer Science · Engineering · Social Sciences · #Adversarial system #Artificial intelligence #Closed captioning #Computational and Text Analysis Methods #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #Generative grammar #Image (mathematics) #Image and Video Processing (eess.IV) #Image translation #Machine Learning (cs.LG) #Multimodal Machine Learning Applications #Object (grammar) #Theme (computing) #Translation (biology) #World Wide Web #cs.CV #cs.LG #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2201.09152
published in arXiv (Cornell University) (Cornell University) · Computational Science and Computational Intelligence; 2021 International Conference on IEEE CPS (IEEE XPLORE, Scopus), IEEE, 2021
arxiv created 2022/01/23 · openalex publication_date 2022/01/23 · arxiv updated 2022/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.