General Heterogeneous Transfer Distance Metric Learning via Knowledge Fragments Transfer

By Yong Luo, Yonggang Wen, Tongliang Liu, Dacheng Tao. 2017

12 citations recorded by the source corpus.

Transfer learning aims to improve the performance of target learning task by leveraging information (or transferring knowledge) from other related tasks. Recently, transfer distance metric learning (TDML) has attracted lots of interests, but most of these methods assume that feature representations for the source and target learning tasks are the same. Hence, they are not suitable for the applications, in which the data are from heterogeneous domains (feature spaces, modalities and even semantics). Although some existing heterogeneous transfer learning (HTL) approaches is able to handle such domains, they lack flexibility in real-world applications, and the learned transformations are often restricted to be linear. We therefore develop a general and flexible heterogeneous TDML (HTDML) framework based on the knowledge fragment transfer strategy. In the proposed HTDML, any (linear or nonlinear) distance metric learning algorithms can be employed to learn the source metric beforehand. Then a set of knowledge fragments are extracted from the pre-learned source metric to help target metric learning. In addition, either linear or nonlinear distance metric can be learned for the target domain. Extensive experiments on both scene classification and object recognition demonstrate superiority of the proposed method.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Chang Xu
3. Yunhe Wang
4. Chao Xu
5. Tongliang Liu
6. Mingming Gong
7. Ya Li
8. Xinmei Tian

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. Chang Xu
3. Yunhe Wang
4. Chao Xu
5. Tongliang Liu
6. Mingming Gong
7. Xinmei Tian
8. Ya Li

In Radiology, Nuclear Medicine and Imaging

1. Dacheng Tao
2. Chang Xu
3. Yunhe Wang

Citations in the corpus

• Domain Generalization via Conditional Invariant Representations (2018, background): “Domain adaptation is a related problem which has been extensively studied in the literature (Baktashmotlagh et al. 2013; Huang et al. 2007; Pan et al. 2011; Long et al. 2017; Shao, Kit, and Fu 2014; Shao et al. 2016; Luo et al. 2017; Liu, Yang, and Tao 2017; ?”
• Adversarial Learning of Portable Student Networks (2018, background): “…recently, i.e., directly learning a portable network (student network) with fewer parameters to inherit valuable properties of the original network (teacher network) (Hinton, Vinyals, and Dean 2015; Romero et al. 2014), which has the similar purpose to transfer learning (Luo et al. 2014; 2017).”