Unsupervised domain adaptation aims to transfer knowledge from a source
domain to a target domain so that the target domain data can be recognized
without any explicit labelling information for this domain. One limitation of
the problem setting is that testing data, despite having no labels, from the
target domain is needed during training, which prevents the trained model being
directly applied to classify unseen test instances. We formulate a new
cross-domain classification problem arising from real-world scenarios where
labelled data is available for a subset of classes (known classes) in the
target domain, and we expect to recognize new samples belonging to any class
(known and unseen classes) once the model is learned. This is a generalized
zero-shot learning problem where the side information comes from the source
domain in the form of labelled samples instead of class-level semantic
representations commonly used in traditional zero-shot learning. We present a
unified domain adaptation framework for both unsupervised and zero-shot
learning conditions. Our approach learns a joint subspace from source and
target domains so that the projections of both data in the subspace can be
domain invariant and easily separable. We use the supervised locality
preserving projection (SLPP) as the enabling technique and conduct experiments
under both unsupervised and zero-shot learning conditions, achieving
state-of-the-art results on three domain adaptation benchmark datasets:
Office-Caltech, Office31 and Office-Home.

该论文提出了基于领域自适应的跨域分类问题，通过联合子空间的学习，使用有标签样本辅助实现跨域特征投影不变性，并结合了一种监督的局部保持投影 (SLPP) 技术，同时在无监督和零样本学习条件下，在三个跨域自适应基准数据集上实现了最先进的结果。