What question did this study set out to answer?

The focus is to address the prediction challenges posed by rare data in deep imbalance regression.

April 24, 2026Artificial Intelligence Review0 citationsOpen Access

Deconstructing deep imbalance regression: a comprehensive review and experimental evaluation

NPNoah C. Puetz JBJens U. Brandt MHMarc HilbertLeiden University

Key Points

The focus is to address the prediction challenges posed by rare data in deep imbalance regression.
Reviewed 19 state-of-the-art deep imbalance regression methods.
Categorized challenges along Data and Deep-Learning Axes.
Developed three novel evaluation protocols for targeted testing.
Identified significant impacts of data imbalance on regression performance.
Provided a conceptual framework to enhance model reliability for rare events.
Re-evaluated methods to expose hidden failure modes under controlled conditions.

Abstract

Abstract In real-world applications, there is a fundamental problem: the data most critical to predict interesting events, anomalies, and high-stakes outliers are the rarest, while less interesting data is abundant. Although deep learning is deployed specifically for these difficult prediction tasks, data-driven models inevitably fail in underrepresented areas. This discrepancy between the empirical data- and the desired evaluation distribution is equivalent to a target distribution shift. The research field, termed Deep Imbalanced Regression (DIR), has emerged explicitly to address this challenge, which is particularly acute for continuous targets where most conventional classification-based methods are ill-suited. In this paper, we present the first comprehensive review of the DIR landscape, organized around a novel two-axis taxonomy that disentangles challenges along a Data Axis (target distribution shift, continuity, and density) and a Deep-Learning Axis (shared capacity, biased updates, and manifold distortion), where the latter captures a cascading failure mechanism through which deep models systematically neglect underrepresented targets. Within this framework, we systematically categorize and analyze 19 state-of-the-art methods spanning architectural, algorithm-level, and representation learning approaches, and empirically re-evaluate twelve of them with publicly available implementations under controlled, identical conditions. To stress-test generalization across the full target range, we introduce three novel targeted evaluation protocols, Balanced Extrapolation, Bimodal Interpolation, and Blind-Spot Isolation, that expose failure modes hidden by standard benchmarks (https: //github. com/noah-puetz/deconstructingdeepᵢmbalancedᵣegression). Our study underscores the significant impact of imbalance on regression accuracy, offering a conceptual framework and practical benchmarks to catalyze further development of systems capable of capturing the rare as reliably as the common.

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