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. |
|---|---|
| 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] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193310383 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deconstructing deep imbalanced regression: a comprehensive review and experimental evaluation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Puetz%2C+Noah+C%2E%22">Puetz, Noah C.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> n.c.puetz@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Brandt%2C+Jens+U%2E%22">Brandt, Jens U.</searchLink><relatesTo>1,2,3</relatesTo> (AUTHOR)<i> j.u.brandt@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Hilbert%2C+Marc%22">Hilbert, Marc</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> Marc.Hilbert@toyota-racing.com</i><br /><searchLink fieldCode="AR" term="%22Raponi%2C+Elena%22">Raponi, Elena</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> e.raponi@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Bäck%2C+Thomas%22">Bäck, Thomas</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> T.H.W.Baeck@liacs.leidenuniv.nl</i><br /><searchLink fieldCode="AR" term="%22Bartz-Beielstein%2C+Thomas%22">Bartz-Beielstein, Thomas</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> thomas.bartz-beielstein@th-koeln.de</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Jun2026, Vol. 59 Issue 6, p1-65. 65p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Data+distribution%22">Data distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Evaluation+methodology%22">Evaluation methodology</searchLink><br /><searchLink fieldCode="DE" term="%22Outlier+detection%22">Outlier detection</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-026-11570-1 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 65 StartPage: 1 Subjects: – SubjectFull: Data distribution Type: general – SubjectFull: Evaluation methodology Type: general – SubjectFull: Outlier detection Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Deconstructing deep imbalanced regression: a comprehensive review and experimental evaluation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Puetz, Noah C. – PersonEntity: Name: NameFull: Brandt, Jens U. – PersonEntity: Name: NameFull: Hilbert, Marc – PersonEntity: Name: NameFull: Raponi, Elena – PersonEntity: Name: NameFull: Bäck, Thomas – PersonEntity: Name: NameFull: Bartz-Beielstein, Thomas IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 59 – Type: issue Value: 6 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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