论文标题
使用模拟数据生成气候变化的图像
Using Simulated Data to Generate Images of Climate Change
论文作者
论文摘要
域适应任务中使用的生成对抗网络（GAN）具有生成既现实又个性化的图像的能力，可以在维护其可识别特征的同时转换输入图像。但是，他们通常需要大量的培训数据来以健壮的方式产生高质量的图像，这在访问数据受到限制的情况下会限制其可用性。在我们的论文中，我们探讨了使用模拟3D环境中的图像来改善Munit体系结构执行的域适应任务的潜力，旨在利用所产生的图像来提高人们对气候变化的潜在未来影响的认识。
Generative adversarial networks (GANs) used in domain adaptation tasks have the ability to generate images that are both realistic and personalized, transforming an input image while maintaining its identifiable characteristics. However, they often require a large quantity of training data to produce high-quality images in a robust way, which limits their usability in cases when access to data is limited. In our paper, we explore the potential of using images from a simulated 3D environment to improve a domain adaptation task carried out by the MUNIT architecture, aiming to use the resulting images to raise awareness of the potential future impacts of climate change.
