… of deep transfer learning, however, our method jointly learns the low-rank codings and transfers
… By stacking multiple layers’ low-rank coding, we build a deep structure to capture more …

… In fact, even different number of classes will help [4], if we consider the latent low-rank
transfer learning as many-to-many mapping, meaning each class of source data are essentially …

… Abstract—Unsupervised transfer learning has attracted a lot of … transfer learning methods
mainly focus on learning a … an Unsupervised Transfer learning approach based on Low-Rank …

… transfer learning, there are usually limited labeled data in the target domain, whereas in
unsupervised transfer learning … In this paper, we focus on unsupervised transfer learning since it …

… Recent activities on transfer learning attempt to build deep … novel deep transfer low-rank
coding based on deep convolutional neural networks, where we investigate multilayer low-rank …

… After that, we introduce a structural knowledge transfer learning module to mitigate structural
disparity. Taking inspiration from LoRA, we apply low-rank adaptation to the pretrained GNN…

… transfer learning in two directions to compensate missing knowledge from the target domain.
Transfer learning … latent information, we adopt latent low-rank framework to recover missing …

… is preferred by the low-rank constraint. This block … low-rank transfer subspace learning (LTSL)
technique, which generalizes traditional subspace learning techniques to transfer learning …

… spAce-based tRaNsfer lEaRning (LEARNER) for improving estimation of a low-rank matrix
in … This approach leverages similarity in the latent factors in the underlying low-rank structure …

… However, the low-rank structure alone might be too restricted to learn the target domain
knowledge that might lie out of this low-rank subspace, and we extend another sparse residual …