Pairwise Learning Increases Data Efficiency in Deep Neural Networks
Deep neural networks often fail at small-sample regression, collapsing to low-variance predictions that miss individual differences. Here, we introduce a pairwise comparison framework that reframes regression as aggregated relative rankings. This method improves data efficiency across model architectures and dataset sizes. On the UTKFace dataset (23k images), pairwise learning generates over one billion training comparisons without additional supervision. The paired ranking allows a lightweight ResNet10 trained from scratch on home hardware to achieve a 3.64-year MAE-surpassing previous models, including those with large-scale pretraining. For classification into categories, pairwise supe...
| Authors | Robert Bain, Marjan Rashidi, Elizabeth Chrastil |
|---|---|
| Primary category | cs.LG |
| Published | 20 October 2026 |
| Last updated at source | 20 October 2026 |
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