Domain adaptation by mixture of alignments of second- or higher-order scatter tensors
In this paper, we propose an approach to the domain adaptation, dubbed Second-or Higher-order Transfer of Knowledge (So-HoT), based on the mixture of alignments of second-or higher-order scatter statistics between the source and target domains. The human ability to learn from few labeled samples is a recurring motivation in the literature for domain adaptation. Towards this end, we investigate the supervised target scenario for which few labeled target training samples per category exist....[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017|
|Book Title:||30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017|
|01_Koniusz_Domain_adaptation_by_mixture_2017.pdf||605.74 kB||Adobe PDF||Request a copy|
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