most citedUDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction

2 citations · 2 across the 1 of their papers we have counts for

collaborators

1 paper

cs.LG2023★ 2 cited

UDAMA: Unsupervised Domain Adaptation through Multi-discriminator Adversarial Training with Noisy Labels Improves Cardio-fitness Prediction

Yu Wu, Dimitris Spathis, Hong Jia +5

Deep learning models have shown great promise in various healthcare monitoring applications. However, most healthcare datasets with high-quality (gold-standard) labels are small-sc…