Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning

no code implementations • 3 Dec 2021 • Shengjia Zhang, Tiancheng Lin, Yi Xu

To avoid overfitting on source domain, at the second stage, we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain.

Pseudo Label Unsupervised Domain Adaptation