Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks

Victor Schmidt
Victor Schmidt
Alexandra Luccioni
Alexandra Luccioni
S. Karthik Mukkavilli
S. Karthik Mukkavilli
Narmada Balasooriya
Narmada Balasooriya
Kris Sankaran
Kris Sankaran

arXiv: Computer Vision and Pattern Recognition, 2019.

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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

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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