Unsupervised domain adaptation (UDA) is to learn classification models that
make predictions for unlabeled data on a target domain, given labeled data on a
source domain whose distribution diverges from the target one. Mainstream UDA
methods strive to learn domain-aligned features such that classifiers trained
on the source features can be readily applied to the target ones. Although
impressive results have been achieved, these methods have a potential risk of
damaging the intrinsic data structures of target discrimination, raising an
issue of generalization particularly for UDA tasks in an inductive setting. To
address this issue, we are motivated by a UDA assumption of structural
similarity across domains, and propose to directly uncover the intrinsic target
discrimination via constrained clustering, where we constrain the clustering
solutions using structural source regularization that hinges on the very same
assumption. Technically, we propose a hybrid model of Structurally Regularized
Deep Clustering, which integrates the regularized discriminative clustering of
target data with a generative one, and we thus term our method as H-SRDC. Our
hybrid model is based on a deep clustering framework that minimizes the
Kullback-Leibler divergence between the distribution of network prediction and
an auxiliary one, where we impose structural regularization by learning
domain-shared classifier and cluster centroids. By enriching the structural
similarity assumption, we are able to extend H-SRDC for a pixel-level UDA task
of semantic segmentation. We conduct extensive experiments on seven UDA
benchmarks of image classification and semantic segmentation. With no explicit
feature alignment, our proposed H-SRDC outperforms all the existing methods
under both the inductive and transductive settings. We make our implementation
codes publicly available at this https URL

使用深度聚类框架，基于结构相似性约束的聚类算法，对无标签目标领域进行无监督域适应任务，能够在图像分类和语义分割领域内的无显式特征对齐的情况下，实现优异表现