Research map / Label-efficient and robust learning / Recognition and long-tail
Improve Representation for Imbalanced Regression through Geometric Constraints
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's full text. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Proposes enveloping and homogeneity geometric losses on a hypersphere latent trace, applied via a surrogate-driven representation learning (SRL) framework, to improve deep imbalanced regression, and introduces the IOL/OL-DIR operator-learning benchmark.
Problem
Deep imbalanced regression lacks analysis of how representations of under-represented label ranges are distributed over the latent hypersphere; classification-style uniformity (distinct clusters) does not fit continuous, ordered regression representations.
Approach
Treat regression representations as a continuous latent trace on the unit hypersphere and enforce uniformity via an enveloping loss (tubular-neighborhood volume coverage of the hypersphere) and a homogeneity loss (arc-length penalty for smooth, evenly spaced representations), computed on a memory-based surrogate of per-bin centroids rather than on mini-batches.
Key results
- On AgeDB-DIR, SRL achieves MAE 7.22 overall and 9.81 few-shot vs. 7.67 and 12.61 for vanilla MSE.
- On IMDB-WIKI-DIR, SRL reaches MAE 7.69 overall vs. 8.03 for vanilla; few-shot MAE 22.78 vs. 26.11.
- On STS-B-DIR, SRL improves Pearson correlation to 0.765 overall vs. 0.742 for vanilla; few-shot Pearson 0.844 vs. 0.793.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add SRL geometric constraints to the regression representation learner | UCI-DIR · UCI Airfoil, UCI Abalone, UCI Real Estate, and UCI Concrete | MAE (All): 5.23 -> 5.10; 4.33 -> 4.16; 0.33 -> 0.28; 6.71 -> 5.94 | Helped |
| Use SRL instead of the strongest listed baseline | AgeDB-DIR · AgeDB-DIR | MAE (All): 7.41 -> 7.22 | Helped |
| Use SRL instead of the strongest listed baseline | IMDB-WIKI-DIR · IMDB-WIKI-DIR | MAE (All): 7.72 -> 7.69 | Helped |
| Use SRL instead of RankSim | STS-B-DIR · STS-B-DIR | Pearson correlation (All): 0.763 -> 0.765 | Helped |
| Add SRL to the operator-learning model | OL-DIR · OL-DIR Linear and Nonlinear | MAE (10−3, All): 10.07 -> 9.18; 11.64 -> 11.25 | Helped |
| Add the enveloping loss (ablation) | SRL · AgeDB-DIR | MAE | Helped |
| Add the homogeneity loss (ablation) | SRL · AgeDB-DIR | MAE | Helped |
| Add enveloping, homogeneity, and contrastive loss terms together rather than using each individually (ablation) | SRL · AgeDB-DIR and IMDB-WIKI-DIR | model performance | Helped |
| Use a sufficiently large number of sampled points N for enveloping-loss estimation (ablation) | SRL · AgeDB-DIR and IMDB-WIKI-DIR | model performance | Helped |
| Increase the number of bins (ablation) | SRL · DIR datasets | model performance | Mixed |
Surprising findings
- Using homogeneity loss alone causes feature collapse to trivial shapes (circle or point), so enveloping loss is needed as the main term.
- Few-shot proportion of features on AgeDB-DIR rose from 1.98% to 15.80% with SRL, which is well above the 1.10% sample share.
Limitations
- Cannot handle higher-dimensional labels (stated in supplementary limitations)
- Requires binning of continuous labels for geometric loss computation
- ConR omitted for tabular and text tasks because it relies on data augmentation
Experimental setup
Datasets: AgeDB-DIR IMDB-WIKI-DIR STS-B-DIR UCI-DIR OL-DIR
Benchmarks: AgeDB-DIR IMDB-WIKI-DIR STS-B-DIR UCI-DIR OL-DIR
Metrics: MAE geometric mean MSE Pearson correlation
Baselines: VANILLA MSE LDS + FDS RankSim BalancedMSE Ordinal Entropy ConR
Builds on: Balanced MSE RankSim LDS/FDS ConR Wang and Isola uniformity
Closest papers by meaning
- PRIME: Deep Imbalanced Regression with Proxies (ICML 2025 · similarity 0.87)
- Leveraging Group Classification with Descending Soft Labeling for Deep Imbalanced Regression (AAAI 2025 · similarity 0.86)
- ConR: Contrastive Regularizer for Deep Imbalanced Regression (ICLR 2024 · similarity 0.86)
- Balancing the Scales: A Theoretical and Algorithmic Framework for Learning from Imbalanced Data (ICML 2025 · similarity 0.85)
- Deep Imbalanced Regression via Hierarchical Classification Adjustment (CVPR 2024 · similarity 0.85)
- Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance (ICML 2024 · similarity 0.84)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.