Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the available data is scarce. The effectiveness of the transfer is affected by the relationship between source and target. Rather than improving the learning, brute force leveraging of a source poorly related to the target may decrease the classifier performance. One strategy to reduce this negative transfer is to import knowledge from multiple sources to increase the chance of finding one source closely related to the target. In (Yao & Doretto, 2010) these ideas are explored by extending the boosting framework for transferring knowledge from multiple sources. It turns out that it is possible to obtain algorithms that are very efficient in terms of the speed with which they can be retrained once a new target domain is given. Such algorithms have been applied to very important computer vision problems such as object category recognition and specific object detection.


  1. CVPR
    Boosting for transfer learning with multiple sources Yao, Y., and Doretto, G. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. abstract bibTeX pdf