This work provides a framework for addressing the problem of supervised
domain adaptation with deep models. The main idea is to exploit adversarial
learning to learn an embedded subspace that simultaneously maximizes the
confusion between two domains while semantically aligning their embedding. The
supervised setting becomes attractive especially when there are only a few
target data samples that need to be labeled. In this few-shot learning
scenario, alignment and separation of semantic probability distributions is
difficult because of the lack of data. We found that by carefully designing a
training scheme whereby the typical binary adversarial discriminator is
augmented to distinguish between four different classes, it is possible to
effectively address the supervised adaptation problem. In addition, the
approach has a high speed of adaptation, i.e. it requires an extremely low
number of labeled target training samples, even one per category can be
effective. We then extensively compare this approach to the state of the art in
domain adaptation in two experiments: one using datasets for handwritten digit
recognition, and one using datasets for visual object recognition.

采用对抗训练来学习嵌入的子空间，同时最大化两个域之间的混淆并在语义上对齐它们，解决了少量有标签目标数据样本的深度模型的有监督域自适应问题。

少样本对抗性域自适应

Few-Shot Adversarial Domain Adaptation

This work provides a unified framework for addressing the problem of visual
supervised domain adaptation and generalization with deep models. The main idea
is to exploit the Siamese architecture to learn an embedding subspace that is
discriminative, and where mapped visual domains are semantically aligned and
yet maximally separated. The supervised setting becomes attractive especially
when only few target data samples need to be labeled. In this scenario,
alignment and separation of semantic probability distributions is difficult
because of the lack of data. We found that by reverting to point-wise
surrogates of distribution distances and similarities provides an effective
solution. In addition, the approach has a high speed of adaptation, which
requires an extremely low number of labeled target training samples, even one
per category can be effective. The approach is extended to domain
generalization. For both applications the experiments show very promising
results.

本文提出了一个视觉监督领域适应和泛化深度模型的统一框架，利用孪生网络结构来学习判别性嵌入子空间，通过点对代理的分布距离和相似性来解决少量目标数据样本标注的情况下，语义概率分布的对齐与分离问题。研究表明该方法在快速自适应方面的优势表现出来，并且只需要极少量的标注目标训练样本，甚至每个类别仅需一个标注样本就可以发挥有效性。此外，该方法还可扩展到领域泛化，并且实验证明该方法在两个应用领域都有很好的效果。