Adaptive SVM+: Learning with Privileged Information for Domain Adaptation

By Nikolaos Sarafianos, Michalis Vrigkas, Ioannis A. Kakadiaris. 2017

25 citations recorded by the source corpus.

Incorporating additional knowledge in the learning process can be beneficial for several computer vision and machine learning 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, using such privileged (i.e., auxiliary) 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, which poses an additional challenge to the recognition task. To address these challenges, we present a novel learning paradigm that leverages privileged information in a domain adaptation setup to perform visual recognition tasks. The proposed framework, named Adaptive SVM+, combines the advantages of both the learning using privileged information (LUPI) 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 publicly available Animals with Attributes and INTERACT datasets and report state-of-the-art results in both of them.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Few-Shot Adversarial Domain Adaptation (2017, methodology): “In addition to these methods, [6, 36, 50] have addressed UDA when an auxiliary data view [31, 37], is available during training, but that is beyond the scope of this work.”