Deconstructing deep imbalanced regression: a comprehensive review and experimental evaluation.
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| Title: | Deconstructing deep imbalanced regression: a comprehensive review and experimental evaluation. |
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| Authors: | Puetz, Noah C.1,2,3 (AUTHOR) n.c.puetz@liacs.leidenuniv.nl, Brandt, Jens U.1,2,3 (AUTHOR) j.u.brandt@liacs.leidenuniv.nl, Hilbert, Marc2,3 (AUTHOR) Marc.Hilbert@toyota-racing.com, Raponi, Elena2 (AUTHOR) e.raponi@liacs.leidenuniv.nl, Bäck, Thomas2 (AUTHOR) T.H.W.Baeck@liacs.leidenuniv.nl, Bartz-Beielstein, Thomas1 (AUTHOR) thomas.bartz-beielstein@th-koeln.de |
| Source: | Artificial Intelligence Review. Jun2026, Vol. 59 Issue 6, p1-65. 65p. |
| Subjects: | Data distribution, Evaluation methodology, Outlier detection, Machine learning, Deep learning |
| 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/deconstructing%5fdeep%5fimbalanced%5fregression). 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. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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