Adaptive SVM+: Learning With Privileged Information for Domain Adaptation

Nikolaos Sarafianos, Michalis Vrigkas, Ioannis A. Kakadiaris; Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017, pp. 2637-2644

Incorporating additional knowledge in the learning process can be beneficial for several computer vision tasks. Whether privileged information originates from a source domain that is adapted to a target domain, or as additional features available at training time only, utilizing such privileged information is of high importance as it improves the recognition performance and generalization. However, both primary and privileged information are rarely derived from the same distribution. In this paper, we present a novel learning paradigm that leverages privileged information in a domain adaptation setup. The proposed framework named Adaptive SVM+ combines the advantages of both the learning using privileged information paradigm and the domain adaptation framework, which are naturally embedded in the objective function of a regular SVM. We demonstrate the effectiveness of our approach on the Animals with Attributes and INTERACT datasets and report state-of-the-art results in both of them.