Transferring Annotator- and Instance-dependent Transition Matrix for Learning from Crowds

arXiv:2306.03116v3 Announce Type: replace-cross
Abstract: Learning from crowds describes that the annotations of training data are obtained with crowd-sourcing services. Multiple annotators each complete their own small part of the annotations, where labeling mistakes that depend on annotators occur frequently. Modeling the label-noise generation process by the noise transition matrix is a power tool to tackle the label noise. In real-world crowd-sourcing scenarios, noise transition matrices are both annotator- and instance-dependent. However, due to the high complexity of annotator- and …

abstract annotations arxiv crowd-sourcing cs.ai cs.hc cs.lg data instance labeling matrix mistakes modeling multiple noise part process services small training training data transition type