Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
arXiv: Computer Vision and Pattern Recognition, 2019.
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs). By training our CycleGAN model on street-view images of houses before and after extreme weather events (e.g. floods, forest fires, etc.), we learn a mapping t...More
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