… is large and the transfer functions are usually not explicitly obtained. In this work, we borrow
the idea of deep learning and propose a deep transfer metric learning method by learning a …

… In this paper, we introduce listwise ranking loss into deep metric learning, and utilize it to
transfer the soft similarities between candidates and the query into student models. …

… Unsupervised metric learning attempts to learn a low-… metric learning, which is the
mainstream metric learning technique and the focus in this article, seeks an appropriate metric by …

… transfer metric learning (RTML) framework to effectively assist the unlabeled target learning
… Specifically, RTML exploits knowledge transfer to mitigate the domain shift in two directions, …

… For this purpose, a multi-modal cascade detection framework of pipeline defects based
on Deep Transfer Metric Learning (DTML) is proposed for defect recognition and defect size …

… of a transfer learning strategy and metric learning techniques to build a Deep Convolutional
… the problem of over-time learning, so the proposed framework can learn to identify new web …

… deep metric learning approaches, such as discriminative deep metric learning and deep
transfer metric learning… of samples by learning hierarchical nonlinear transformations. However, …

… utilized in metric learning to address this problem. In recent years, deep metric learning, which
… This article aims to reveal the importance of deep metric learning and the problems dealt …

… Unlike these deep learning based trackers, in this paper, we present a deep metric learning
approach to learn a set of hierarchical nonlinear transformations for visual tracking. Our …

… Hence, the metric learning is casted as learning combination … using source metrics to
help the target metric learning, but … Ji, “Constrained deep transfer feature learning and its …