Learning Attributes from the Crowdsourced Relative Labels_看图王.pdf


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2024-03-17
attributes labels propose de Systems Bioinformatics Key Center Tech. aggregate
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Learning Attributes from
the Crowdsourced Relative Labels
Tian Tian,† Ning Chen,‡ Jun Zhu†
†Dept. of Comp. Sci. & Tech., CBICR Center, State Key Lab for Intell. Tech. & Systems
‡MOE Key lab of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology
TNList, Tsinghua University, Beijing, China
{tiant16@mails, ningchen@, dcszj@}tsinghua.edu.cn
Abstract
Finding semantic attributes to describe related concepts is typ-
ically a hard problem. The commonly used attributes in most
fields are designed by domain experts, which is expensive
and time-consuming. In this paper we propose an efficient
method to learn human comprehensible attributes with crowd-
sourcing. We first design an analogical interface to collect
relative labels from the crowds. Then we propose a hierar-
chical Bayesian model, as well as an efficient initialization
strategy, to aggregate labels and extract concise attributes. Our
experimental results demonstrate promise on discovering


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