We introduce the novel problem of inter-robot transfer learning for perceptual classification of objects, where multiple heterogeneous robots communicate and transfer learned obje...
Cross-domain learning methods have shown promising
results by leveraging labeled patterns from auxiliary domains
to learn a robust classifier for target domain, which
has a limi...
Dong Xu, Ivor Wai-Hung Tsang, Lixin Duan, Stephen ...
Learning transfer is the improvement in performance on one task having learnt a related task. That the degree of transfer is signi cantly greater in humans than other primates and...
We propose a spectral learning approach to shape segmentation. The method is composed of a constrained spectral clustering algorithm that is used to supervise the segmentation of a...
Transfer learning addresses the problem of how to utilize plenty of labeled data in a source domain to solve related but different problems in a target domain, even when the train...