论文标题
视频生成对抗网络：评论
Video Generative Adversarial Networks: A Review
论文作者
论文摘要
随着对媒体，教育和娱乐等多个领域的内容创建领域的兴趣日益增加，论文中使用AI算法来生成诸如图像，视频，音频和文本之类的内容的趋势越来越多。在一个有希望的模型之一中，生成对抗网络（GAN）合成了与实际数据样本相似的数据样本。据我们所知，虽然一般而言，甘斯模型的变化在某种程度上已被涵盖，但这是审查最先进的视频gans模型的首批调查论文之一。本文首先将gans评论论文分为一般gans审查论文，图像gans审查论文和特殊野外审查论文，例如异常检测，医学成像或网络安全。然后，本文总结了最初为视频域开发但已在多种视频gans变化中采用的gans框架的主要改进。然后，根据条件的存在或非列表，在两个主要部门下进行了对视频剂模型的全面审查。然后，条件模型然后根据条件类型进一步分组为音频，文本，视频和图像。该论文的结论是强调当前视频gans模型的主要挑战和局限性。补充材料中提供了全面的数据集，应用损失功能和评估指标。
With the increasing interest in the content creation field in multiple sectors such as media, education, and entertainment, there is an increasing trend in the papers that uses AI algorithms to generate content such as images, videos, audio, and text. Generative Adversarial Networks (GANs) in one of the promising models that synthesizes data samples that are similar to real data samples. While the variations of GANs models, in general, have been covered to some extent in several survey papers, to the best of our knowledge, this is among the first survey papers that reviews the state-of-the-art video GANs models. This paper first categorized GANs review papers into general GANs review papers, image GANs review papers, and special field GANs review papers such as anomaly detection, medical imaging, or cybersecurity. The paper then summarizes the main improvements in GANs frameworks that are not initially developed for the video domain but have been adopted in multiple video GANs variations. Then, a comprehensive review of video GANs models is provided under two main divisions according to the presence or non-presence of a condition. The conditional models then further grouped according to the type of condition into audio, text, video, and image. The paper is concluded by highlighting the main challenges and limitations of the current video GANs models. A comprehensive list of datasets, applied loss functions, and evaluation metrics is provided in the supplementary material.
