# ConR: Contrastive Regularizer for Deep Imbalanced Regression

arXiv: https://arxiv.org/abs/2309.06651 · HTML: https://mlsanity.com/paper/2309.06651/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 13 Sep 2023.

## Builds on (most cited first, 24 of 24)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Improved Baselines with Momentum Contrastive Learning](https://mlsanity.com/paper/2003.04297/) (2020)
- [Decoupling Representation and Classifier for Long-Tailed Recognition](https://mlsanity.com/paper/1910.09217/) (2019)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Long-tailed Recognition by Routing Diverse Distribution-Aware Experts](https://mlsanity.com/paper/2010.01809/) (2020)
- [What is the Effect of Importance Weighting in Deep Learning?](https://mlsanity.com/paper/1812.03372/) (2018)
- [Parametric Contrastive Learning](https://mlsanity.com/paper/2107.12028/) (2021)
- [Delving into Deep Imbalanced Regression](https://mlsanity.com/paper/2102.09554/) (2021)
- [Revisiting Single Image Depth Estimation: Toward Higher Resolution Maps with Accurate Object Boundaries](https://mlsanity.com/paper/1803.08673/) (2018)
- [Feature Space Augmentation for Long-Tailed Data](https://mlsanity.com/paper/2008.03673/) (2020)
- [Targeted Supervised Contrastive Learning for Long-Tailed Recognition](https://mlsanity.com/paper/2111.13998/) (2021)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Balanced MSE for Imbalanced Visual Regression](https://mlsanity.com/paper/2203.16427/) (2022)
- [Rank-N-Contrast: Learning Continuous Representations for Regression](https://mlsanity.com/paper/2210.01189/) (2022)
- [Posterior Re-calibration for Imbalanced Datasets](https://mlsanity.com/paper/2010.11820/) (2020)
- [RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression](https://mlsanity.com/paper/2205.15236/) (2022)
- [Moving Window Regression: A Novel Approach to Ordinal Regression](https://mlsanity.com/paper/2203.13122/) (2022)
- [Improving Contrastive Learning on Imbalanced Seed Data via Open-World Sampling](https://mlsanity.com/paper/2111.01004/) (2021)
- [Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed Classification](https://mlsanity.com/paper/2112.07928/) (2021)
- [Contrastive learning for regression in multi-site brain age prediction](https://mlsanity.com/paper/2211.08326/) (2022)

## Built on by (most cited first, 19 of 19)

- [IM-Context: In-Context Learning for Imbalanced Regression Tasks](https://mlsanity.com/paper/2405.18202/) (2024)
- [Improve Representation for Imbalanced Regression through Geometric Constraints](https://mlsanity.com/paper/2503.00876/) (2025)
- [Data Augmentation with Variational Autoencoder for Imbalanced Dataset](https://mlsanity.com/paper/2412.07039/) (2024)
- [Dist Loss: Enhancing Regression in Few-Shot Region through Distribution Distance Constraint](https://mlsanity.com/paper/2411.15216/) (2024)
- [Gradient Aligned Regression via Pairwise Losses](https://mlsanity.com/paper/2402.06104/) (2024)
- [PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation](https://mlsanity.com/paper/2502.08106/) (2025)
- [Improving Deep Regression with Tightness](https://mlsanity.com/paper/2502.09122/) (2025)
- [Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression](https://mlsanity.com/paper/2412.12327/) (2024)
- [Disentangled Deep Smoothed Bootstrap for Fair Imbalanced Regression](https://mlsanity.com/paper/2508.13829/) (2025)
- [Balanced Sharpness-Aware Minimization for Imbalanced Regression](https://mlsanity.com/paper/2508.16973/) (2025)
- [Provably Minimum-Length Conformal Prediction Sets for Ordinal Classification](https://mlsanity.com/paper/2511.16845/) (2025)
- [Highly Imbalanced Regression with Tabular Data in SEP and Other Applications](https://mlsanity.com/paper/2509.16339/) (2025)
- [More Data or Better Algorithms: Latent Diffusion Augmentation for Deep Imbalanced Regression](https://mlsanity.com/paper/2509.23240/) (2025)
- [NearID: Identity Representation Learning via Near-identity Distractors](https://mlsanity.com/paper/2604.01973/) (2026)
- [Improving Lexical Difficulty Prediction with Context-Aligned Contrastive Learning and Ridge Ensembling](https://mlsanity.com/paper/2605.08950/) (2026)
- [Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement](https://mlsanity.com/paper/2606.21287/) (2026)
- [Stochastic Order Learning: An Approach to Rank Estimation Using Noisy Data](https://mlsanity.com/paper/2607.08103/) (2026)
- [Contrastive Order Learning: A General Framework for Ordinal Regression](https://mlsanity.com/paper/2607.08109/) (2026)
- [Beyond Homoscedasticity: Decoupled Uncertainty Optimization for Deep Imbalanced Regression](https://mlsanity.com/paper/2609.04995/) (2026)

## Abstract (arXiv)

Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep Neural Networks to represent the minority labels and avoid bias towards majority labels. The extensive body of imbalanced approaches address categorical label spaces but fail to effectively extend to regression problems where the label space is continuous. Local and global correlations among continuous labels provide valuable insights towards effectively modelling relationships in feature space. In this work, we propose ConR, a contrastive regularizer that models global and local label similarities in feature space and prevents the features of minority samples from being collapsed into their majority neighbours. ConR discerns the disagreements between the label space and feature space and imposes a penalty on these disagreements. 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 model local similarities. ConR consolidates essential considerations into a generic, easy-to-integrate, and efficient method that effectively addresses deep imbalanced regression. Moreover, ConR is orthogonal to existing approaches and smoothly extends to uni- and multi-dimensional label spaces. Our comprehensive experiments show that ConR significantly boosts the performance of all the state-of-the-art methods on four large-scale deep imbalanced regression benchmarks. Our code is publicly available in https://github.com/BorealisAI/ConR.