Papers › Improving Deep Regression with Ordinal Entropy
Improving Deep Regression with Ordinal Entropy
Shihao Zhang, Linlin Yang, Michael Bi Mi, Xiaoxu Zheng, Angela Yao
In computer vision, it is often observed that formulating regression problems as a classification task often yields better performance. We investigate this curious phenomenon and provide a derivation to show that classification, with the cross-entropy loss, outperforms regression with a mean squared error loss in its ability to learn high-entropy feature representations. Based on the analysis, we propose an ordinal entropy loss to encourage higher-entropy feature spaces while maintaining ordinal relationships to improve the performance of regression tasks. Experiments on synthetic and real-world regression tasks demonstrate the importance and benefits of increasing entropy for regression.
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Code
Syntology Ran 4 of 5 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong; 1 ran · fixture could not drive it.
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Code Syntology ran Syntology
5 samples harvested; 4 ran; 1 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Harvested from needylove/ordinalentropy. Some samples are identical code Syntology first harvested from another repository; for those, this paper's copy is not located and its licence is not recorded. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Crowd Counting | ShanghaiTech A | OrdinalEntropy | MAE | 65.6 | #20 of 35 | Archive leaderboard | report |
| Crowd Counting | ShanghaiTech A | OrdinalEntropy | MSE | 105.0 | #20 of 35 | Archive leaderboard | report |
| Crowd Counting | ShanghaiTech B | OrdinalEntropy | MAE | 9.1 | #20 of 32 | Archive leaderboard | report |
| Crowd Counting | ShanghaiTech B | OrdinalEntropy | MSE | 14.5 | #20 of 32 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | OrdinalEntropy | Delta < 1.25 | 0.932 | #37 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | OrdinalEntropy | RMSE | 0.321 | #37 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | OrdinalEntropy | absolute relative error | 0.089 | #37 of 85 | Archive leaderboard | report |
| Monocular Depth Estimation | NYU-Depth V2 | OrdinalEntropy | log 10 | 0.039 | #37 of 85 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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