Using Simulated Data to Generate Images of Climate Change
Mila（米拉）；Université de Montréal（蒙特利尔大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
