English(EN) Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression

新的DUO框架通过不确定性优化解决深度不平衡回归问题

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-07 04:00

研究人员推出了一种新颖的DUO框架，旨在改进深度不平衡回归任务。DUO通过将回归目标建模为条件高斯分布来解决长尾数据中的异方差性问题，明确表征实例级别的预测不确定性。该方法通过解耦的均值-方差优化将不确定性转化为尾部样本的动态增强信号。此外，DUO还引入了一个由分布引导的对比学习机制来优化特征表示和语义纠缠，在IMDB-WIKI-DIR、AgeDB-DIR和AAV2-DIR等基准测试中表现出色。 AI

影响增强了用于处理不平衡数据的深度学习模型，有可能提高年龄估计和蛋白质活性预测等领域的准确性。

排序理由该集群包含一篇详细介绍深度不平衡回归新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Juncheng Zhou, Jiaxi Lu, Weijing Zeng, Zhong Li, Hao Qi, Jingsong Cui · 2026-09-07 04:00

超越同方差性：深度不平衡回归的解耦不确定性优化

arXiv:2609.04995v1 Announce Type: new Abstract: Deep Imbalanced Regression (DIR) is pervasive in continuous prediction tasks across diverse modalities, such as age estimation, depth prediction, and protein mutation activity prediction, where label-scarce tail samples often carry …

本文如何被排名

报道来源 [1]

超越同方差性：深度不平衡回归的解耦不确定性优化

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