March 13, 2024, 4:43 a.m. | Xiaoqian Ruan, Gaoang Wang

cs.LG updates on

arXiv:2112.15411v2 Announce Type: replace
Abstract: Large-scale datasets are important for the development of deep learning models. Such datasets usually require a heavy workload of annotations, which are extremely time-consuming and expensive. To accelerate the annotation procedure, multiple annotators may be employed to label different subsets of the data. 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 …

abstract annotation annotations arxiv bias cs.lg data datasets deep learning development however multiple regression scale subsets type

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