面向多源标注的不相交对比回归学习
Disjoint Contrastive Regression Learning for Multi-Sourced Annotations
AI总结：
针对多标注员不相交标注存在的标注偏差问题，提出结合对比排序损失与梯度反转层的对比回归框架，兼顾标注内一致性与标注间不变性，在表情预测和图像质量评估任务上验证了有效性。
摘要：
大规模数据集对深度学习模型的发展至关重要，这类数据集通常需要繁重的标注工作量，既耗时又成本高昂。为加快标注流程，研究者可能会雇佣多名标注员为数据的不同子集打标。但不同标注员之间的不一致性和偏差会损害模型训练，尤其对于定性和主观任务而言影响更甚。为解决这一挑战，本文提出一种新型对比回归框架，用于处理不相交标注问题——即每个样本仅由一名标注员标注，多名标注员负责互不相交的数据子集。为同时兼顾标注员内部一致性与标注员间不一致性，框架采用两种策略：其一，应用基于对比的损失学习同一标注员标注的不同样本间的相对排序，前提假设是同一标注员给出的样本排序具有一致性；其二，应用梯度反转层（gradient reversal layer）学习对不同标注员具有不变性的鲁棒表征。在面部表情预测任务与图像质量评估任务上的实验，验证了所提框架的有效性。
英文摘要：
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 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. To take account of both the intra-annotator consistency and inter-annotator inconsistency, two strategies are employed.Firstly, a contrastive-based loss is applied to learn the relative ranking among different samples of the same annotator, with the assumption that the ranking of samples from the same annotator is unanimous. Secondly, we apply the gradient reversal layer to learn robust representations that are invariant to different annotators. Experiments on the facial expression prediction task, as well as the image quality assessment task, verify the effectiveness of our proposed framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
