Transfer learning aims to learn classifiers for a target domain by transferring knowledge from a source domain. However, due to two main issues: feature discrepancy and distribution divergence, transfer learning can be a very difficult problem in practice. In this paper, we present a framework called TLF that builds a classifier for the target domain having only few labeled training records by transferring knowledge from the source domain having many labeled records. While existing methods often focus on one issue and leave the other one for the further work, TLF is capable of handling both issues simultaneously. In TLF, we alleviate feature discrepancy by identifying shared label distributions that act as the pivots to bridge the domains. We handle distribution divergence by simultaneously optimizing the structural risk functional, joint distributions between domains, and the manifold consistency underlying marginal distributions. Moreover, for the manifold consistency we exploit its intrinsic properties by identifying k nearest neighbors of a record, where the value of k is determined automatically in TLF. Furthermore, since negative transfer is not desired, we consider only the source records that are belonging to the source pivots during the knowledge transfer. We evaluate TLF on seven publicly available natural datasets and compare the performance of TLF against the performance of fourteen state-of-the-art techniques. We also evaluate the effectiveness of TLF in some challenging situations. Our experimental results, including statistical sign test and Nemenyi test analyses, indicate a clear superiority of the proposed framework over the state-of-the-art techniques.
Enabling Heterogeneous Domain Adaptation in Multi-inhabitants Smart Home Activity Learning
University of Massachusetts Lowell（马萨诸塞大学洛厄尔分校）；University of California, San Diego（加利福尼亚大学圣迭戈分校）；IBM’s Thomas J. Watson Research Center（IBM托马斯·J·沃森研究中心）；University of Massachusetts Chan Medical School（马萨诸塞大学陈医学院）
DIB-OD: Preserving the Invariant Core for Robust Heterogeneous Graph Adaptation via Decoupled Information Bottleneck and Online Distillation
School of Information Technology and Engineering, Tianjin University of Technology and Education（天津职业技术师范大学信息技术工程学院）；School of Computer Science and Technology, Tiangong University（天工大学计算机科学与技术学院）；Department of Computer Science at University of Exeter（埃克塞特大学计算机科学系）
Un-mixing Test-time Adaptation under Heterogeneous Data Streams
Beijing Academy of Artificial Intelligence（北京人工智能研究院）；Alibaba Group（阿里巴巴集团）；Xi’an Jiaotong-Liverpool University（西安交通大学利物浦大学）；Duke Kunshan University（杜克大学昆山分校）
Driver Drowsiness Estimation from EEG Signals Using Online Weighted Adaptation Regularization for Regression (OwARR)
DataNova；U.S. Army Research Laboratory（美国陆军研究实验室）；University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；DCS Corp（DCS公司）；University of Technology, Sydney（悉尼科技大学）；National Chiao-Tung University（国立交通大学）
FedHL: Federated Learning for Heterogeneous Low-Rank Adaptation via Unbiased Aggregation
Institute of Artificial Intelligence and Future Networks, Beijing Normal University, Zhuhai（人工智能与未来网络研究院，北京师范大学，珠海）；School of Computer Science and Artificial Intelligence, Zhengzhou University（计算机科学与人工智能学院，郑州大学）；School of Software, Nanchang University（软件学院，南昌大学）
ReLoRA: Knowledge-Reusing Adaptation for Fast Rollout of Evolving LLM Services
School of Computer Science and Technology, University of Science and Technology of China（计算机科学与技术学院，中国科学技术大学）；Suzhou Institute for Advanced Research, University of Science and Technology of China（苏州先进研究院，中国科学技术大学）
Task Groupings Regularization: Data-Free Meta-Learning with Heterogeneous Pre-trained Models
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；College of Computing & Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）；School of Cyber Science and Technology, Sun Yat-sen University（中山大学网络空间科学与技术学院）；University of Maryland, College Park（马里兰大学帕克分校）；Department of Computer Science and Engineering, The Chinese University of Hong Kong（香港中文大学计算机科学与工程学系）
Cross-Domain Structure Preserving Projection for Heterogeneous Domain Adaptation
Department of Computer Science, Durham University（达勒姆大学计算机科学系）
异构领域自适应（HDA）解决了迁移学习问题，其中来自源和目标领域的数据具有不同的模态（例如，文本和图像）或特征尺寸（例如，用不同方法提取的特征）。这对于多模态数据分析是有用的。传统的领域自适应算法假设源样本和目标样本的表示位于同一特征空间，因此很可能无法解决异构领域自适应问题。当代国家的最先进的HDA方法通常是由复杂的优化目标的有利的性能，因此是计算昂贵的和较难推广。为了解决这些问题，我们提出了一种新的跨域结构保持投影（CDSPP）算法的HDA。作为经典LPP到异构域的扩展，CDSPP旨在学习特定于域的投影，将源域和目标域的样本特征映射到公共子空间，从而保持类一致性并充分对齐数据分布。CDSPP是简单的，并通过求解广义特征值问题具有确定性的解决方案。它自然适用于监督HDA，但也已扩展为半监督HDA，其中未标记的目标域样本可用。在HDA的常用基准数据集（即NIC-Caltech，Multilingual Reuters Collection，NUS-WIDE-ImageNet）上进行了广泛的实验，以及我们首次为HDA引入的NIC-Home数据集，因为它的类数量明显多于现有的数据集（65 vs 10，6和8）。监督和半监督HDA的实验结果表明，我们提出的方法对当代国家的最先进的方法的优越性能。
Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective
Beijing Teleinfo Technology Company Ltd.（北京 Teleinfo 科技有限公司）；China Academy of Information and Communications Technology（中国信息通信研究院）；Inspur Computer Technology Co., Ltd.（浪潮计算机技术有限公司）；Southern University of Science and Technology（南方科技大学）；Research Institute of Industrial Internet of Things（工业互联网物联网研究院）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
BNU-HKBU United International College（北京师范大学-香港浸会大学联合国际学院）；City University of Hong Kong（香港城市大学）；The Laboratory for AI-Powered Financial Technologies Limited（人工智能金融科技实验室有限公司）；JD Digits Technology（京东数字科技）
Heterogeneous Model Fusion for Privacy-Aware Multi-Camera Surveillance via Synthetic Domain Adaptation
Department of Computer Science and Information Engineering, National Chung Cheng University（资讯工程系，国立 Chung Cheng 大学）；Department of Computer Science, National Yang Ming Chiao Tung University（计算机科学系，国立阳明交通大学）；National Center for High-Performance Computing（国家高速计算中心）
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
University Paris-Saclay（巴黎萨克雷大学）；Inria（法国国家信息与自动化研究所）；CEA（法国原子能和替代能源委员会）；TAU Inria（特拉维夫大学-法国国家信息与自动化研究所联合机构）；LISN-CNRS（法国国家科学研究中心LISN实验室）；Roche Pharma Research and Early Development（罗氏制药研发部）；Roche Innovation Center Basel（罗氏创新中心巴塞尔分部）；F. Hoffmann–La Roche Ltd（罗氏控股有限公司）
Adaptive Bi-Recommendation and Self-Improving Network for Heterogeneous Domain Adaptation-Assisted IoT Intrusion Detection
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）；University of New Brunswick（新不伦瑞克大学）
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；University of Macau（澳门大学）
