Disjoint Contrastive Regression Learning for Multi-Sourced Annotations

no code implementations • 31 Dec 2021 • Xiaoqian Ruan, Gaoang Wang

However, the inconsistency and bias among different annotators are harmful to the model training, especially for qualitative and subjective tasks. To address this challenge, in this paper, we propose a novel contrastive regression framework to address the disjoint annotations problem, where each sample is labeled by only one annotator and multiple annotators work on disjoint subsets of the data.

Image Quality Assessment regression