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NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels
[article]
2021
arXiv
pre-print
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels is still very limited. Moreover, because of annotators' diverse expertise, crowdsourced labels are often
arXiv:2003.09660v4
fatcat:oabmbah6zrgspdw5mky54qdrne