Broad learning system (BLS) has been proposed for a few years. It demonstrates an effective learning capability for many classification and regression problems. However, BLS and its improved versions are mainly used to deal with unsupervised, supervised and semi-supervised learning problems in a single domain. As far as we know, a little attention is paid to the cross-domain learning ability of BLS. Therefore, we introduce BLS into the field of transfer learning and propose a novel algorithm called domain adaptation broad learning system based on locally linear embedding (DABLS-LLE). The proposed algorithm can learn a robust classification model by using a small part of labeled data from the target domain and all labeled data from the source domain. The proposed algorithm inherits the computational efficiency and learning capability of BLS. Experiments on benchmark dataset (Office-Caltech-10) verify the effectiveness of our approach. The results show that our approach can get better classification accuracy with less running time than many existing transfer learning approaches. It shows that our approach can bring a new superiority for BLS.
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）
Bayesian Low-Rank Factorization for Robust Model Adaptation
Interactive Systems Lab, Karlsruhe Institut of Technology (KIT), Germany（交互系统实验室，卡尔斯鲁厄理工学院（KIT），德国）；InterACT, Carnegie Mellon University (CMU), USA（InterACT，卡内基梅隆大学（CMU），美国）
SkMTEB: Slovak Massive Text Embedding Benchmark and Model Adaptation
Comenius University in Bratislava（布拉迪斯拉发夸美纽斯大学）；Cisco Systems（思科系统）；Technical University of Košice（科希策技术大学）；Kempelen Institute of Intelligent Technologies（肯佩伦智能技术研究所）
