Unified Principles For Multi-Source Transfer Learning Under Label Shifts

We study the label shift problem in multi-source transfer learning and derive new generic principles. Our proposed framework unifies the principles of conditional feature alignment, label distribution ratio estimation, and domain relation weights estimation. Based on inspired practical principles, we provide a unified practical framework for three multi-source label shift transfer scenarios: learning with limited target data, unsupervised domain adaptation, and label partial unsupervised domain adaptation. We evaluate the proposed method on these scenarios by extensive experiments and show that our proposed algorithm can significantly outperform the baselines.