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Learning from crowds describes that the annotations of training data areobtained with crowd-sourcing services. Multiple annotators each complete theirown small part of the annotations, where labeling mistakes that depend onannotators occur frequently. Modeling the label-noise generation process by thenoise transition matrix is a power tool to tackle the label noise. Inreal-world crowd-sourcing scenarios, noise transition matrices are bothannotator- and instance-dependent. However, due to the high complexity ofannotator- and instance-dependent transition matrices (AIDTM), annotationsparsity, which means each annotator only labels a little part of instances,makes modeling AIDTM very challenging. Prior works simplify the problem byassuming the transition matrix is instance-independent or using simpleparametric ways, which lose modeling generality. Motivated by this, we target amore realistic problem, estimating general AIDTM in practice. Without losingmodeling generality, we parameterize AIDTM with deep neural networks. Toalleviate the modeling challenge, we suppose every annotator shares its noisepattern with similar annotators, and estimate AIDTM via knowledge transfer. Wehence first model the mixture of noise patterns by all annotators, and thentransfer this modeling to individual annotators. Furthermore, considering thatthe transfer from the mixture of noise patterns to individuals may cause twoannotators with highly different noise generations to perturb each other, weemploy the knowledge transfer between identified neighboring annotators tocalibrate the modeling. Theoretical analyses are derived to demonstrate thatboth the knowledge transfer from global to individuals and the knowledgetransfer between neighboring individuals can help model general AIDTM.Experiments confirm the superiority of the proposed approach on synthetic andreal-world crowd-sourcing data.

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