ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods
- URL: http://arxiv.org/abs/2110.02871v1
- Date: Wed, 6 Oct 2021 15:54:57 GMT
- Title: ClimateGAN: Raising Climate Change Awareness by Generating Images of
Floods
- Authors: Victor Schmidt, Alexandra Sasha Luccioni, M\'elisande Teng, Tianyu
Zhang, Alexia Reynaud, Sunand Raghupathi, Gautier Cosne, Adrien Juraver, Vahe
Vardanyan, Alex Hernandez-Garcia, Yoshua Bengio
- Abstract summary: We present our solution to simulate photo-realistic floods on authentic images.
We propose ClimateGAN, a model that leverages both simulated and real data for unsupervised domain adaptation and conditional image generation.
- Score: 89.61670857155173
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Climate change is a major threat to humanity, and the actions required to
prevent its catastrophic consequences include changes in both policy-making and
individual behaviour. However, taking action requires understanding the effects
of climate change, even though they may seem abstract and distant. Projecting
the potential consequences of extreme climate events such as flooding in
familiar places can help make the abstract impacts of climate change more
concrete and encourage action. As part of a larger initiative to build a
website that projects extreme climate events onto user-chosen photos, we
present our solution to simulate photo-realistic floods on authentic images. To
address this complex task in the absence of suitable training data, we propose
ClimateGAN, a model that leverages both simulated and real data for
unsupervised domain adaptation and conditional image generation. In this paper,
we describe the details of our framework, thoroughly evaluate components of our
architecture and demonstrate that our model is capable of robustly generating
photo-realistic flooding.
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