Generative Adversarial Network Applications in Creating a Meta-Universe
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Generative Adversarial NetworkImage-to-Image TranslationTranslationVideo CaptioningVideo PredictionSimilar Papers 제목 키워드 기반
High-Resolution CMB Lensing Reconstruction with Deep Learning
Next-generation cosmic microwave background (CMB) surveys are expected to provide valuable information about the primordial universe by creating maps of the mass along the line of sight. Traditional tools for creating th…
Deep LearningGenerative Adversarial NetworkVocal Bursts Intensity PredictionGenerative Adversarial Networks for Astronomical Images Generation
Space exploration has always been a source of inspiration for humankind, and thanks to modern telescopes, it is now possible to observe celestial bodies far away from us. With a growing number of real and imaginary image…
Beyond Reality: The Pivotal Role of Generative AI in the Metaverse
Imagine stepping into a virtual world that's as rich, dynamic, and interactive as our physical one. This is the promise of the Metaverse, and it's being brought to life by the transformative power of Generative Artificia…
Image GenerationText GenerationModeling urbanization patterns with generative adversarial networks
In this study we propose a new method to simulate hyper-realistic urban patterns using Generative Adversarial Networks trained with a global urban land-use inventory. We generated a synthetic urban "universe" that qualit…
Structural Segmentation of the Minimum Set Cover Problem: Exploiting Universe Decomposability for Metaheuristic Optimization
The Minimum Set Cover Problem (MSCP) is a classical NP-hard combinatorial optimization problem with numerous applications in science and engineering. Although a wide range of exact, approximate, and metaheuristic approac…