Web: http://arxiv.org/abs/2206.08105

June 17, 2022, 1:13 a.m. | Delong Chen, Ruizhi Zhou, Yanling Pan, Fan Liu

cs.CV updates on arXiv.org arxiv.org

Flood disasters cause enormous social and economic losses. However, both
traditional physical models and learning-based flood forecasting models require
massive historical flood data to train the model parameters. When come to some
new site that does not have sufficient historical data, the model performance
will drop dramatically due to overfitting. This technical report presents a
Flood Domain Adaptation Network (FloodDAN), a baseline of applying Unsupervised
Domain Adaptation (UDA) to the flood forecasting problem. Specifically,
training of FloodDAN includes two stages: …

arxiv cv domain adaptation forecasting unsupervised

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