This paper which is part of the New Faculty Highlights Invited Speaker Program of AAAI'23, serves as a comprehensive survey of my research in transfer learning by utilizing embedding spaces. The work reviewed in this paper specifically revolves around the inherent challenges associated with continual learning and limited availability of labeled data. By providing an overview of my past and ongoing contributions, this paper aims to present a holistic understanding of my research, paving the way for future explorations and advancements in the field. My research delves into the various settings of transfer learning, including, few-shot learning, zero-shot learning, continual learning, domain adaptation, and distributed learning. I hope this survey provides a forward-looking perspective for researchers who would like to focus on similar research directions.

翻译：本文是AAAI'23新教师亮点特邀演讲项目的一部分，系统综述了我利用嵌入空间进行迁移学习的研究成果。文中回顾的工作主要围绕持续学习与有限标注数据带来的固有挑战展开。通过概述我过去及正在进行的研究贡献，本文旨在呈现对我研究的整体理解，为该领域的未来探索与进步铺平道路。我的研究深入探讨了迁移学习的多种场景，包括小样本学习、零样本学习、持续学习、领域自适应及分布式学习。希望本综述能为关注类似研究方向的研究者提供前瞻性视角。