  The predictive performance of supervised learning algorithms depends on the
quality of labels. In a typical label collection process, multiple annotators
provide subjective noisy estimates of the "truth" under the influence of their
varying skill-levels and biases. Blindly treating these noisy labels as the
ground truth limits the accuracy of learning algorithms in the presence of
strong disagreement. This problem is critical for applications in domains such
as medical imaging where both the annotation cost and inter-observer
variability are high. In this work, we present a method for simultaneously
learning the individual annotator model and the underlying true label
distribution, using only noisy observations. Each annotator is modeled by a
confusion matrix that is jointly estimated along with the classifier
predictions. We propose to add a regularization term to the loss function that
encourages convergence to the true annotator confusion matrix. We provide a
theoretical argument as to how the regularization is essential to our approach
both for the case of single annotator and multiple annotators. Despite the
simplicity of the idea, experiments on image classification tasks with both
simulated and real labels show that our method either outperforms or performs
on par with the state-of-the-art methods and is capable of estimating the
skills of annotators even with a single label available per image.
