semilabeled

KEMA is as accurate as the state of the art, but with the advantage of handling naturally domains of different dimensionality, and not requiring semilabeled examples ( in the Table) to align the domains as JDA. The results obtained when using the deep DeCAF features are reported in Table 4 : a strong improvement in performance is observed for all methods.

2016 February 13, “Kernel Manifold Alignment for Domain Adaptation”, in PLOS ONE, →DOI