We propose a novel Dynamic Restrained Uncertainty Weighting Loss to experimentally handle the problem of balancing the contributions of multiple tasks on the ICML ExVo 2022 Challenge. The multitask aims to recognize expressed emotions and demographic traits from vocal bursts jointly. Our strategy combines the advantages of Uncertainty Weight and Dynamic Weight Average, by extending weights with a restraint term to make the learning process more explainable. We use a lightweight multi-exit CNN architecture to implement our proposed loss approach. The experimental H-Mean score (0.394) shows a substantial improvement over the baseline H-Mean score (0.335).

翻译：我们提出一个新的新颖的动态克制不测的重力损失,以实验性地处理在ICML ExVo 2022挑战中平衡多重任务贡献的问题。多任务的目的是从联合声响中识别表达的情感和人口特征。我们的战略结合了不确定性体重和动态体重平均值的优势,延长加权和限制术语,使学习过程更能解释。我们使用轻量的多输出CNN结构来实施我们拟议的损失方法。实验性的H-MEan分数(0.394)表明比基线H-MEan分数(0.335)。