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.

翻译：无人监督的域域适应(UDA) 指的是在一个目标领域学习模型的问题,在目标领域没有标签数据,因此无法通过在源域中利用附加说明的数据提供信息。大多数深 UDA 方法在单一来源、单一目标假设下运作,即它们假定源和目标样本来自单一分布。然而,在实践中,大多数数据集可以被视为多个域的混合体。在这些情况下,利用传统的单一来源、单一目标的学习分类模型方法可能导致结果不佳。此外,通常很难为所有数据点提供域标签,即:应自动发现潜在域。本文引入了一个新的深度结构,通过在视觉数据集中自动发现潜在域并利用这些信息学习强有力的目标分类器来解决UDA问题。具体地说,我们的架构基于两个主要组成部分,即一个侧分支,自动计算每个样本被分配到其潜在域域域域和新层次,利用域成员信息将CNN内部地貌显示的分布与参考分布相匹配。我们评估了现有域域图方法,以公开形式显示我们现有基准的状态。