Using Simulated Data to Generate Images of Climate Change
arXiv:2001.09531
Abstract
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.
arXiv commentProceeding ML-IRL workshop at ICLR 2020
References in corpus (5)
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- CyCADA: Cycle-Consistent Adversarial Domain Adaptation
- Unsupervised CNN for Single View Depth Estimation: Geometry to the Rescue
- Demystifying MMD GANs
- Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks