Learning From Noisy Singly-labeled Data

TL;DR

This paper introduces a new algorithm for learning from noisy, singly-labeled crowd-sourced data, effectively estimating worker quality and optimizing label allocation to improve classifier performance.

Contribution

It proposes a novel alternating minimization algorithm that estimates worker quality from minimal labels and guides optimal labeling strategies, with theoretical and empirical validation.

Findings

The algorithm can estimate worker quality with only one label per example.

It outperforms existing methods in noisy label scenarios.

Labeling many examples once can be more effective than multiple labels per example when worker quality is high.

Abstract

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.