Transfer learning has achieved promising results by leveraging knowledge from
the source domain to annotate the target domain which has few or none labels.
Existing methods often seek to minimize the distribution divergence between
domains, such as the marginal distribution, the conditional distribution or
both. However, these two distances are often treated equally in existing
algorithms, which will result in poor performance in real applications.
Moreover, existing methods usually assume that the dataset is balanced, which
also limits their performances on imbalanced tasks that are quite common in
real problems. To tackle the distribution adaptation problem, in this paper, we
propose a novel transfer learning approach, named as Balanced Distribution
\underline{A}daptation~(BDA), which can adaptively leverage the importance of
the marginal and conditional distribution discrepancies, and several existing
methods can be treated as special cases of BDA. Based on BDA, we also propose a
novel Weighted Balanced Distribution Adaptation~(W-BDA) algorithm to tackle the
class imbalance issue in transfer learning. W-BDA not only considers the
distribution adaptation between domains but also adaptively changes the weight
of each class. To evaluate the proposed methods, we conduct extensive
experiments on several transfer learning tasks, which demonstrate the
effectiveness of our proposed algorithms over several state-of-the-art methods.

本文提出了一种名为平衡分布适应（BDA）的新型迁移学习方法，该方法可以自适应地利用边际和条件分布差异的重要性，并提出了一种新的加权平衡分布适应（W-BDA）算法来解决转移学习中的类别不平衡问题。在多个迁移学习任务中进行的广泛实验表明，与几种先进方法相比，所提出的算法的有效性。