A Unified Framework for Metric Transfer Learning
By Yonghui Xu, Sinno Jialin Pan, Hui Xiong, Qingyao Wu, Ronghua Luo, Huaqing Min, Hengjie Song. 2017
171 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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
- Neil Houlsby
- Basil Mustafa
- Jindong Wang
- Joan Puigcerver
- Carlos Riquelme
- Yiqiang Chen
- Meiyu Huang
- Daniel Keysers
In Computer Vision and Pattern Recognition
- Neil Houlsby
- Basil Mustafa
- Jindong Wang
- Joan Puigcerver
- Carlos Riquelme
- Dacheng Tao
- Philip S. Yu
- Tao Kong
Citations in the corpus
- A Survey on Deep Transfer Learning (2018, background): “[26] propose a metric transfer learning framework to learn instance weights and a distance of two different domains in a parallel framework to make knowledge transfer across domains more effective.”
- Scalable Transfer Learning with Expert Models (2020, methodology): “One family of methods creates a single training dataset, where source instances are re-weighted according to their relevance (Dai et al., 2007; Pardoe & Stone, 2010; Wan et al., 2011; Xu et al., 2017).”
- Visual Domain Adaptation with Manifold Embedded Distribution Alignment (2018, background): “To this end, existing work can be summarized into two main categories: (a) instance reweighting [9, 39], which reuses samples from the source domain according to some weighting technique; and (b) feature matching, which either performs subspace learning by exploiting the subspace geometrical structu”