  Deep regression models typically learn in an end-to-end fashion without
explicitly emphasizing a regression-aware representation. Consequently, the
learned representations exhibit fragmentation and fail to capture the
continuous nature of sample orders, inducing suboptimal results across a wide
range of regression tasks. To fill the gap, we propose Rank-N-Contrast (RNC), a
framework that learns continuous representations for regression by contrasting
samples against each other based on their rankings in the target space. We
demonstrate, theoretically and empirically, that RNC guarantees the desired
order of learned representations in accordance with the target orders, enjoying
not only better performance but also significantly improved robustness,
efficiency, and generalization. Extensive experiments using five real-world
regression datasets that span computer vision, human-computer interaction, and
healthcare verify that RNC achieves state-of-the-art performance, highlighting
its intriguing properties including better data efficiency, robustness to
spurious targets and data corruptions, and generalization to distribution
shifts. Code is available at: https://github.com/kaiwenzha/Rank-N-Contrast.
