ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods
Clicks: 141
ID: 281668
2021
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
141 views
44 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #15 of 803 articles by views in arXiv
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 803 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
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.
| Reference Key |
bengio2021climategan
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Victor Schmidt; Alexandra Sasha Luccioni; Mélisande Teng; Tianyu Zhang; Alexia Reynaud; Sunand Raghupathi; Gautier Cosne; Adrien Juraver; Vahe Vardanyan; Alex Hernandez-Garcia; Yoshua Bengio |
| Journal | arXiv |
| Year | 2021 |
| DOI |
DOI not found
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.