We study the problem of aggregation noisy labels. Usually, it is solved by proposing a stochastic model for the process of generating noisy labels and then estimating the model parameters using the observed noisy labels. A traditional assumption underlying previously introduced generative models is that each object has one latent true label. In contrast, we introduce a novel latent distribution assumption, implying that a unique true label for an object might not exist, but rather each object might have a specific distribution generating a latent subjective label each time the object is observed. Our experiments showed that the novel assumption is more suitable for difficult tasks, when there is an ambiguity in choosing a "true" label for certain objects.
@article{arxiv.1906.08776,
title = {Latent Distribution Assumption for Unbiased and Consistent Consensus Modelling},
author = {Valentina Fedorova and Gleb Gusev and Pavel Serdyukov},
journal= {arXiv preprint arXiv:1906.08776},
year = {2019}
}