Learning from Crowds by Modeling Common Confusions

TL;DR

This paper introduces a novel model for crowdsourced annotation that distinguishes between common and individual confusions, improving the learning process from noisy, variable-quality labels.

Contribution

It proposes a new crowdsourcing model with noise decomposition and an end-to-end learning framework utilizing shared and individual noise adaptation layers.

Findings

Effective in modeling common and individual confusions.

Improves learning accuracy from crowdsourced annotations.

Validated on synthetic and real-world datasets.

Abstract

Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost. However, the annotation quality of annotators varies considerably, which imposes new challenges in learning a high-quality model from the crowdsourced annotations. In this work, we provide a new perspective to decompose annotation noise into common noise and individual noise and differentiate the source of confusion based on instance difficulty and annotator expertise on a per-instance-annotator basis. We realize this new crowdsourcing model by an end-to-end learning solution with two types of noise adaptation layers: one is shared across annotators to capture their commonly shared confusions, and the other one is pertaining to each annotator to realize individual confusion. To recognize the source of noise in each annotation, we use an auxiliary network to choose the two noise adaptation layers with respect to both instances and annotators. Extensive experiments on both synthesized and real-world benchmarks demonstrate the effectiveness of our proposed common noise adaptation solution.