We propose an active learning approach for transferring representations
across domains. Our approach, active adversarial domain adaptation (AADA),
explores a duality between two related problems: adversarial domain alignment
and importance sampling for adapting models across domains. The former uses a
domain discriminative model to align domains, while the latter utilizes it to
weigh samples to account for distribution shifts. Specifically, our importance
weight promotes samples with large uncertainty in classification and diversity
from labeled examples, thus serves as a sample selection scheme for active
learning. We show that these two views can be unified in one framework for
domain adaptation and transfer learning when the source domain has many labeled
examples while the target domain does not. AADA provides significant
improvements over fine-tuning based approaches and other sampling methods when
the two domains are closely related. Results on challenging domain adaptation
tasks, e.g., object detection, demonstrate that the advantage over baseline
approaches is retained even after hundreds of examples being actively
annotated.

提出了一种积极学习的方法，通过敌对领域自适应（AADA）进行表示转移，其探索了两个相关问题之间的双重性：敌对领域对齐和重要性采样来适应跨域模型， 以及将两种方法结合在一个框架中进行领域自适应和转移学习，当源域有许多标记示例而目标域没有时，它提供了重要的改进。

主动对抗域自适应

Active Adversarial Domain Adaptation

Recently, considerable effort has been devoted to deep domain adaptation in
computer vision and machine learning communities. However, most of existing
work only concentrates on learning shared feature representation by minimizing
the distribution discrepancy across different domains. Due to the fact that all
the domain alignment approaches can only reduce, but not remove the domain
shift. Target domain samples distributed near the edge of the clusters, or far
from their corresponding class centers are easily to be misclassified by the
hyperplane learned from the source domain. To alleviate this issue, we propose
to joint domain alignment and discriminative feature learning, which could
benefit both domain alignment and final classification. Specifically, an
instance-based discriminative feature learning method and a center-based
discriminative feature learning method are proposed, both of which guarantee
the domain invariant features with better intra-class compactness and
inter-class separability. Extensive experiments show that learning the
discriminative features in the shared feature space can significantly boost the
performance of deep domain adaptation methods.

通过联合域对齐和辨别特征学习的方式，可以带来对域对齐和最终任务分类的双重优化。对于视觉和机器学习领域，大部分现有工作只集中在通过最小化不同域之间的分布差异来学习共享的特征表示。然而，本文指出，所有域对齐方法只能减少，而不是消除域移位的情况。因此，我们提出了一种实例和中心的辨别特征学习方法，它们都可以在共享的特征空间中学习到具有更好的类内紧密性和类间可分性的域不变特征。实验表明，在共享的特征空间中学习辨别特征可以显著提高深度域自适应方法的性能。