Robust Deep Learning from Crowds with Belief Propagation
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Crowdsourcing systems enable us to collect noisy labels from crowd workers. A graphical model representing local dependencies between workers and tasks provides a principled way of reasoning over the true labels from the noisy answers. However, one needs a predictive model working on unseen data directly from crowdsourced datasets instead of the true labels in many cases. To infer true labels and learn a predictive model simultaneously, we propose a new data-generating process, where a neural network generates the true labels from task features. We devise an EM framework alternating variational inference and deep learning to infer the true labels and to update the neural network, respectively. Experimental results with synthetic and real datasets show a belief-propagation-based EM algorithm is robust to i) corruption in task features, ii) multi-modal or mismatched worker prior, and iii) few spammers submitting noises to many tasks.
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