Current Domain Adaptation (DA) methods based on deep architectures assume
that the source samples arise from a single distribution. However, in practice,
most datasets can be regarded as mixtures of multiple domains. In these cases
exploiting single-source DA methods for learning target classifiers may lead to
sub-optimal, if not poor, results. In addition, in many applications it is
difficult to manually provide the domain labels for all source data points,
i.e. latent domains should be automatically discovered. This paper introduces a
novel Convolutional Neural Network (CNN) architecture which (i) automatically
discovers latent domains in visual datasets and (ii) exploits this information
to learn robust target classifiers. Our approach is based on the introduction
of two main components, which can be embedded into any existing CNN
architecture: (i) a side branch that automatically computes the assignment of a
source sample to a latent domain and (ii) novel layers that exploit domain
membership information to appropriately align the distribution of the CNN
internal feature representations to a reference distribution. We test our
approach on publicly-available datasets, showing that it outperforms
state-of-the-art multi-source DA methods by a large margin.

本文提出了一种基于深度卷积神经网络的领域自适应方法，在自动发现图像数据集中的潜在领域并利用此信息来学习鲁棒的目标分类器方面取得了显著优于现有方法的结果，其中引入了两个主要组件，一个是自动计算源样本分配到潜在领域的支路，另一个是利用领域成员信息对齐 CNN 内部特征表示分布的新型图层。