Become A Wiser, More Profitable Investor

\emph{MEnsA}: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds. (arXiv:2304.01554v1 [cs.CV])

science

Unsupervised domain adaptation (UDA) addresses the problem of distribution shift between the unlabeled target domain and labelled source domain. While the single target domain adaptation (STDA) is well studied in both 2D and 3D vision literature, multi-target domain adaptation (MTDA) is barely explored for 3D data despite its wide real-world applications such as autonomous driving systems for various geographical and climatic conditions. We establish an MTDA baseline for 3D point cloud data by proposing to mix the feature representations from all domains together to achieve better domain adaptation performance by an ensemble average, which we call \emph{{\bf M}ixup {\bf Ens}emble {\bf A}verage} or {\bf \emph{MEnsA}}.

arXiv     Apr 05, 2023