Abstract
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.
Abstract (translated)
无监督域适应的目的是将知识从源域转移到目标域，这样目标域数据就可以被识别，而无需对此域进行任何明确的标记信息。问题设置的一个限制是，在培训期间，尽管没有标签，但仍需要来自目标域的测试数据，这会阻止直接应用培训模型对未看到的测试实例进行分类。我们提出了一个新的跨域分类问题，该问题源于现实场景，其中标记数据可用于目标域中类（已知类）的子集，我们希望在学习模型后识别属于任何类（已知类和未知类）的新样本。这是一个广义的零镜头学习问题，其中边信息以标记样本的形式来自源域，而不是传统零镜头学习中常用的类级语义表示。我们提出了一个统一的领域适应框架的无监督和零镜头学习条件。我们的方法从源域和目标域学习一个联合子空间，这样子空间中的两个数据的投影都可以是域不变的，并且容易分离。我们以受监督的位置保持投影（SLPP）为启用技术，在无监督和零镜头学习条件下进行实验，在三个领域适应基准数据集（Office Caltech、Office31和Office Home）上获得最新的结果。
URL
https://arxiv.org/abs/1903.10601
