Domain adaptation has shown appealing performance by leveraging knowledge
from a source domain with rich annotations. However, for a specific target
task, it is cumbersome to collect related and high-quality source domains. In
real-world scenarios, large-scale datasets corrupted with noisy labels are easy
to collect, stimulating a great demand for automatic recognition in a
generalized setting, i.e., weakly-supervised partial domain adaptation
(WS-PDA), which transfers a classifier from a large source domain with noises
in labels to a small unlabeled target domain. As such, the key issues of WS-PDA
are: 1) how to sufficiently discover the knowledge from the noisy labeled
source domain and the unlabeled target domain, and 2) how to successfully adapt
the knowledge across domains. In this paper, we propose a simple yet effective
domain adaptation approach, termed as self-paced transfer classifier learning
(SP-TCL), to address the above issues, which could be regarded as a
well-performing baseline for several generalized domain adaptation tasks. The
proposed model is established upon the self-paced learning scheme, seeking a
preferable classifier for the target domain. Specifically, SP-TCL learns to
discover faithful knowledge via a carefully designed prudent loss function and
simultaneously adapts the learned knowledge to the target domain by iteratively
excluding source examples from training under the self-paced fashion. Extensive
evaluations on several benchmark datasets demonstrate that SP-TCL significantly
outperforms state-of-the-art approaches on several generalized domain
adaptation tasks.

本文介绍了一种简单而有效的基于自适应学习的域自适应方法，称为自适应迁移分类器学习（SP-TCL），用于解决弱监督下的部分域自适应问题，通过精心设计的损失函数发现准确知识并在遵循自适应学习方案下从源域中迭代地排除示例，实现了目标域的成功适应，多项基准测试结果表明 SP-TCL 在多个广义域适应任务中明显优于现有方法。