Domain Adaptation Meets Active Learning

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Domain adaptation meets active learning. In this work we show how active learning in some target domain can leverage information from a different but related source domain. Piyush rai avishek saha hal daumé suresh venkatasubramanian. This paper presents a novel technique for addressing domain adaptation da problems with active learning al in the classification of remote sensing images. Request pdf domain adaptation meets active learning in this work we show how active learning in some target domain can leverage information from a different but related source domain.

We present an algorithm that harnesses the source domain data to learn the best possible initializer. Domain adaptation meets active learning. Domain adaptation meets active learning piyush rai avishek saha hal daum e iii and suresh venkatasubramanian school of computing university of utah salt lake city ut 84112 piyush avishek hal suresh cs utah edu abstract in this work we show how active learning in some target domain can leverage infor mation from a different but. Active learning aims to minimize labeling effort by selecting the most informative.

Domain adaptation meets active learning piyush rai avishek saha hal daum e iii and suresh venkatasubramanian school of computing university of utah salt lake city ut 84112 fpiyush avishek hal suresh g cs utah edu abstract in this work we show how active learning in some target domain can leverage infor. Ieee international conference on computer vision iccv 2017. Domain adaptation meets active learning. Previous chapter next chapter.

Abstract in this work we show how active learning in some target domain can leverage information from a different but related source domain. When unsupervised domain adaptation meets tensor representations. Da models the important problem of adapting a supervised classifier trained on a given image source domain to the classification of another similar but not identical image target domain acquired on a different area. Domain adaptation meets active learning.

Los angeles california venues. In this work we show how active learning in some target domain can leverage information from a different but related source domain. In this paper we apply active learning strategies to domain adaptation for named entity recognition systems and show that adaptive learning combining the source and target domains is more effective than non adaptive learning directly from the target domain. By piyush rai avishek saha hal daumé iii and suresh venkatasubramanian.

By hao lu 1 lei zhang 2 zhiguo cao 1 wei wei 2 ke xian 1 chunhua shen 3 anton van den hengel 3.