IEEE Trans Pattern Anal Mach Intell. 2021 Feb;43(2):485-498. doi: 10.1109/TPAMI.2019.2933829. Epub 2021 Jan 8.
Unsupervised Domain Adaptation (UDA) refers to the problem of learning a model in a target domain where labeled data are not available by leveraging information from annotated data in a source domain. Most deep UDA approaches operate in a single-source, single-target scenario, i.e., they assume that the source and the target samples arise from a single distribution. However, in practice most datasets can be regarded as mixtures of multiple domains. In these cases, exploiting traditional single-source, single-target methods for learning classification models may lead to poor results. Furthermore, it is often difficult to provide the domain labels for all data points, i.e. latent domains should be automatically discovered. This paper introduces a novel deep architecture which addresses the problem of UDA by automatically discovering latent domains in visual datasets and exploiting this information to learn robust target classifiers. Specifically, our architecture is based on two main components, i.e. a side branch that automatically computes the assignment of each sample to its latent domain and novel layers that exploit domain membership information to appropriately align the distribution of the CNN internal feature representations to a reference distribution. We evaluate our approach on publicly available benchmarks, showing that it outperforms state-of-the-art domain adaptation methods.
无监督领域自适应（UDA）是指在目标域中学习模型的问题，其中没有可用的标记数据，而是利用源域中注释数据的信息。大多数深度 UDA 方法都在单源、单目标场景中运行，即它们假设源和目标样本来自单个分布。然而，在实践中，大多数数据集可以被视为多个域的混合物。在这些情况下，利用传统的单源、单目标方法来学习分类模型可能会导致较差的结果。此外，通常很难为所有数据点提供域标签，即应该自动发现潜在域。本文提出了一种新的深度架构，通过自动发现视觉数据集中的潜在域，并利用该信息学习鲁棒的目标分类器，来解决 UDA 问题。具体来说，我们的架构基于两个主要组件，即一个侧分支，它自动计算每个样本到其潜在域的分配，以及新的层，利用域成员信息来适当地将 CNN 内部特征表示的分布与参考分布对齐。我们在公开可用的基准上评估了我们的方法，结果表明它优于最先进的领域自适应方法。
