  Despite plenty of efforts focusing on improving the domain adaptation ability
(DA) under unsupervised or few-shot semi-supervised settings, recently the
solution of active learning started to attract more attention due to its
suitability in transferring model in a more practical way with limited
annotation resource on target data. Nevertheless, most active learning methods
are not inherently designed to handle domain gap between data distribution, on
the other hand, some active domain adaptation methods (ADA) usually requires
complicated query functions, which is vulnerable to overfitting. In this work,
we propose a concise but effective ADA method called
Select-by-Distinctive-Margin (SDM), which consists of a maximum margin loss and
a margin sampling algorithm for data selection. We provide theoretical analysis
to show that SDM works like a Support Vector Machine, storing hard examples
around decision boundaries and exploiting them to find informative and
transferable data. In addition, we propose two variants of our method, one is
designed to adaptively adjust the gradient from margin loss, the other boosts
the selectivity of margin sampling by taking the gradient direction into
account. We benchmark SDM with standard active learning setting, demonstrating
our algorithm achieves competitive results with good data scalability. Code is
available at https://github.com/TencentYoutuResearch/ActiveLearning-SDM
