This paper describes an active transfer learning technique for multi-view head-pose classification. We combine transfer learning with active learning, where an active learner asks the domain expert to label the few most informative target samples for transfer learning. Employing adaptive multiple-kernel learning (AMKL) for head-pose classification from four low-resolution views, we show how active sampling enables more efficient learning with few examples. Experimental results confirm that active transfer learning produces 10% higher pose-classification accuracy over several competing transfer learning approaches.

Active Transfer Learning for Multi-View Head-Pose Classification

Lanz, Oswald;
2012

Abstract

This paper describes an active transfer learning technique for multi-view head-pose classification. We combine transfer learning with active learning, where an active learner asks the domain expert to label the few most informative target samples for transfer learning. Employing adaptive multiple-kernel learning (AMKL) for head-pose classification from four low-resolution views, we show how active sampling enables more efficient learning with few examples. Experimental results confirm that active transfer learning produces 10% higher pose-classification accuracy over several competing transfer learning approaches.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11582/103201
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact