论文标题
创建元宇宙的生成对抗网络应用程序
Generative Adversarial Network Applications in Creating a Meta-Universe
论文作者
论文摘要
生成的对抗网络（GAN）是在许多重要和新颖应用中使用的机器学习方法。例如，在成像科学中，有效地利用了gan来生成图像数据集，人脸的照片，图像和视频字幕，图像到图像翻译，文本到图像翻译，视频预测和3D对象生成等。在本文中，我们讨论如何使用甘恩来创建人造世界。更具体地说，我们讨论了gan如何使用图像/视频字幕方法来描述图像，以及如何使用我们想要的主题中的图像到图像翻译框架将图像转换为新图像。我们阐明甘恩如何影响创造自定义世界。
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.
