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Large-scale datasets are important for the development of deep learningmodels. Such datasets usually require a heavy workload of annotations, whichare extremely time-consuming and expensive. To accelerate the annotationprocedure, multiple annotators may be employed to label different subsets ofthe data. However, the inconsistency and bias among different annotators areharmful to the model training, especially for qualitative and subjectivetasks.To address this challenge, in this paper, we propose a novel contrastiveregression framework to address the disjoint annotations problem, where eachsample is labeled by only one annotator and multiple annotators work ondisjoint subsets of the data. To take account of both the intra-annotatorconsistency and inter-annotator inconsistency, two strategies areemployed.Firstly, a contrastive-based loss is applied to learn the relativeranking among different samples of the same annotator, with the assumption thatthe ranking of samples from the same annotator is unanimous. Secondly, we applythe gradient reversal layer to learn robust representations that are invariantto different annotators. Experiments on the facial expression prediction task,as well as the image quality assessment task, verify the effectiveness of ourproposed framework.

Disjoint Contrastive Regression Learning for Multi-Sourced Annotations