Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks

John Daniel Bossér; Erik Sörstadius; Morteza Haghir Chehreghani

doi:10.1109/BigData52589.2021.9671795

2021 IEEE International Conference on Big Data (Big Data)

Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks

Year: 2021, Pages: 5053-5062

DOI Bookmark: 10.1109/BigData52589.2021.9671795

Authors

John Daniel Bossér, Chalmers University of Technology, Linkoping University
Erik Sörstadius, Chalmers University of Technology
Morteza Haghir Chehreghani, Chalmers University of Technology

Abstract

In step with the rapid proliferation of mobile devices with Internet access, the Web is increasingly being access by mobile-device users on the move. Further, it is increasingly possible to accurately geo-position mobile devices, and increasing volumes of geo-positioned content, e.g., Web pages, business directory entries, and microblog posts, are becoming available on the Web. In short, an increasingly mobile and spatial Web is fast emerging. This development enables Web queries with local intent, i.e., keyword-based queries issued by users who are looking for Web content near them. In addition, it implies an increasing demand for query functionality that supports local intent.

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Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks

Authors

Abstract

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