  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.
