Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks
Athanasios Davvetas,
Iraklis A. Klampanos
Abstract:Acquiring ground truth labels for unlabelled data can be a costly procedure, since it often requires manual labour that is error-prone. Consequently, the available amount of labelled data is increasingly reduced due to the limitations of manual data labelling. It is possible to increase the amount of labelled data samples by performing automated labelling or crowd-sourcing the annotation procedure. However, they often introduce noise or uncertainty in the labelset, that leads to decreased performance of superv… Show more
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