Supervised learning depends on annotated examples, which are taken to be the ground truth. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechanical Turk. Practitioners typically collect multiple labels per example and aggregate the results to mitigate noise (the classic crowdsourcing problem). Given a fixed annotation budget and unlimited unlabeled data, redundant annotation comes at the expense of fewer labeled examples. This raises two fundamental questions: (1) How can we best learn from noisy workers? (2) How should we allocate our labeling budget to maximize the performance of a classifier? We propose a new algorithm for jointly modeling labels and worker quality from noisy crowd-sourced data. The alternating minimization proceeds in rounds, estimating worker quality from disagreement with the current model and then updating the model by optimizing a loss function that accounts for the current estimate of worker quality. Unlike previous approaches, even with only one annotation per example, our algorithm can estimate worker quality. We establish a generalization error bound for models learned with our algorithm and establish theoretically that it's better to label many examples once (vs less multiply) when worker quality exceeds a threshold. Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.
A new approach for learning a model from noisy crowdsourced annotations. This paper proposes a method for learning from noisy labels, focusing on the case when data isn't redundantly labeled with theoretical and experimental validation This paper focuses on the learning-from-crowds problem, where jointly updating the classifier weights and the confusion matrices of workers can help on the estimation problem with rare crowdsourced labels. Proposes a supervised learning algorithm for modeling label and worker quality and utilizes algorithm to study how much redundancy is required in crowdsourcing and whether low redundancy with abundant noise examples lead to better labels.