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Found 2 papers, 2 papers with code
A Simple Baseline for Adversarial Domain Adaptation-based Unsupervised Flood Forecasting
Specifically, training of FloodDAN includes two stages: in the first stage, we train a rainfall encoder and a prediction head to learn general transferable hydrological knowledge on large-scale source domain data; in the second stage, we transfer the knowledge in the pretrained encoder into the rainfall encoder of target domain through adversarial domain alignment.
Self-Supervised Music-Motion Synchronization Learning for Music-Driven Conducting Motion Generation
Therefore, we propose a novel Music Motion Synchronized Generative Adversarial Network (M2S-GAN), which generates motions according to the automatically learned music representations.
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