While unsupervised domain adaptation methods based on deep architectures have
achieved remarkable success in many computer vision tasks, they rely on a
strong assumption, i.e. labeled source data must be available. In this work we
overcome this assumption and we address the problem of transferring knowledge
from a source to a target domain when both source and target data have no
annotations. Inspired by recent works on deep clustering, our approach
leverages information from data gathered from multiple source domains to build
a domain-agnostic clustering model which is then refined at inference time when
target data become available. Specifically, at training time we propose to
optimize a novel information-theoretic loss which, coupled with
domain-alignment layers, ensures that our model learns to correctly discover
semantic labels while discarding domain-specific features. Importantly, our
architecture design ensures that at inference time the resulting source model
can be effectively adapted to the target domain without having access to source
data, thanks to feature alignment and self-supervision. We evaluate the
proposed approach in a variety of settings, considering several domain
adaptation benchmarks and we show that our method is able to automatically
discover relevant semantic information even in presence of few target samples
and yields state-of-the-art results on multiple domain adaptation benchmarks.

本文提出了一种基于无监督域自适应和深度聚类的方法，利用来自多个源域的数据信息建立一个与具体域无关的聚类模型，并通过特征对齐和自我监督实现有效地适应目标域。本文的方法即使在少量目标样本的情况下也能自动发现相关语义信息，并在多个域自适应基准测试中取得了最先进的结果。