Deep imbalanced regression

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (7 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Deep Imbalanced Regression to Estimate Vascular Age from PPG Data: a Novel Digital Biomarker for Cardiovascular Health
  21 Jun 2024 2 repositories listed
  Photoplethysmography (PPG) is emerging as a crucial tool for monitoring human hemodynamics, with recent studies highlighting its potential in assessing vascular aging through deep learning.
• Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression
  16 Dec 2024 1 repository listed
  Deep imbalanced regression (DIR), where the target values have a highly skewed distribution and are also continuous, is an intriguing yet under-explored problem in machine learning.
• IM-Context: In-Context Learning for Imbalanced Regression Tasks
  28 May 2024 1 repository listed
  In this paper, we study the impact of the prompt sequence on the model performance from both theoretical and empirical perspectives.
• ConR: Contrastive Regularizer for Deep Imbalanced Regression
  13 Sep 2023 1 repository listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  ConR addresses the continuous nature of label space with two main strategies in a contrastive manner: incorrect proximities are penalized proportionate to the label similarities and the correct ones are encouraged to…
• RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression
  30 May 2022 1 repository listed Syntology ran 3 of 4 samples · 1 unverified · 4 pointer-only (licence)
  Data imbalance, in which a plurality of the data samples come from a small proportion of labels, poses a challenge in training deep neural networks.
• Delving into Deep Imbalanced Regression
  18 Feb 2021 1 repository listed Syntology ran 2 of 2 samples · 0 unverified
  We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.

Syntology lines on 3 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.