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Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression

arXiv · AI, language, vision and robotics · article · Sep 4, 2026 · UTC

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 higher practical value. However, most existing methods still learn deterministic point mappings under mean squared error or its simple variants, implicitly assuming a uniform uncertainty level across all samples and thereby overlooking the instance-wise heteroscedasticity that is widespread in long-tailed data. We further point out that even heteroscedastic negative

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