Learning From Noisy Singly-labeled Data
This addresses the problem of efficient annotation budget allocation for practitioners using noisy crowdsourcing platforms, offering a novel method that is incremental in improving upon existing crowdsourcing approaches.
The paper tackles learning from noisy crowdsourced labels by proposing an algorithm that jointly models labels and worker quality, enabling estimation even with single annotations per example, and shows theoretically and experimentally that labeling many examples once is better than fewer multiply when worker quality is above a threshold, with experiments on ImageNet and MS-COCO confirming benefits.
Supervised learning depends on annotated examples, which are taken to be the \emph{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 is above a threshold. Experiments conducted on both ImageNet (with simulated noisy workers) and MS-COCO (using the real crowdsourced labels) confirm our algorithm's benefits.