The application of transfer learning, an approach utilizing knowledge from a
source domain to enhance model performance in a target domain, has seen a
tremendous rise in recent years, underpinning many real-world scenarios. The
key to its success lies in the shared common knowledge between the domains, a
prerequisite in most transfer learning methodologies. These methods typically
presuppose identical feature spaces and label spaces in both domains, known as
homogeneous transfer learning, which, however, is not always a practical
assumption. Oftentimes, the source and target domains vary in feature spaces,
data distributions, and label spaces, making it challenging or costly to secure
source domain data with identical feature and label spaces as the target
domain. Arbitrary elimination of these differences is not always feasible or
optimal. Thus, heterogeneous transfer learning, acknowledging and dealing with
such disparities, has emerged as a promising approach for a variety of tasks.
Despite the existence of a survey in 2017 on this topic, the fast-paced
advances post-2017 necessitate an updated, in-depth review. We therefore
present a comprehensive survey of recent developments in heterogeneous transfer
learning methods, offering a systematic guide for future research. Our paper
reviews methodologies for diverse learning scenarios, discusses the limitations
of current studies, and covers various application contexts, including Natural
Language Processing, Computer Vision, Multimodality, and Biomedicine, to foster
a deeper understanding and spur future research.

综述了异构迁移学习方法的最新发展，为未来的研究提供了系统的指南，包括自然语言处理、计算机视觉、多模态和生物医学等各种应用场景。

异构迁移学习综述

A Survey on Heterogeneous Transfer Learning

Distance metric learning (DML) plays a crucial role in diverse machine
learning algorithms and applications. When the labeled information in target
domain is limited, transfer metric learning (TML) helps to learn the metric by
leveraging the sufficient information from other related domains. Multi-task
metric learning (MTML), which can be regarded as a special case of TML,
performs transfer across all related domains. Current TML tools usually assume
that the same feature representation is exploited for different domains.
However, in real-world applications, data may be drawn from heterogeneous
domains. Heterogeneous transfer learning approaches can be adopted to remedy
this drawback by deriving a metric from the learned transformation across
different domains. But they are often limited in that only two domains can be
handled. To appropriately handle multiple domains, we develop a novel
heterogeneous multi-task metric learning (HMTML) framework. In HMTML, the
metrics of all different domains are learned together. The transformations
derived from the metrics are utilized to induce a common subspace, and the
high-order covariance among the predictive structures of these domains is
maximized in this subspace. There do exist a few heterogeneous transfer
learning approaches that deal with multiple domains, but the high-order
statistics (correlation information), which can only be exploited by
simultaneously examining all domains, is ignored in these approaches. Compared
with them, the proposed HMTML can effectively explore such high-order
information, thus obtaining more reliable feature transformations and metrics.
Effectiveness of our method is validated by the extensive and intensive
experiments on text categorization, scene classification, and social image
annotation.

提出一种新的异构多任务度量学习框架（HMTML），利用多个不同子领域数据学习跨域度量，并在共同子空间中最大化预测结构的高阶协方差，进而有效地探索高阶信息，验证实验表明其方法具有更可靠的特征转换和度量。