Rank-N-Contrast: Learning Continuous Representations for Regression

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 . L 1 Temp. (℃) 30 20 10 0 SupCon R N C (Ours) Figure 1: Learned representations of different methods on a real-world temperature regression task [7] (details in Sec. 5). Existing general regression learning (L1) or representation learning (SupCon) schemes fail to recognize the underlying continuous information in data. In contrast, RNC learns continuous representations that capture the intrinsic sample orders w.r.t. the regression targets.