Deep regression models typically learn in an end-to-end fashion and do not
explicitly try to learn a regression-aware representation. Their
representations tend to be fragmented and fail to capture the continuous nature
of regression tasks. In this paper, we propose Supervised Contrastive
Regression (SupCR), a framework that learns a regression-aware representation
by contrasting samples against each other based on their target distance. SupCR
is orthogonal to existing regression models, and can be used in combination
with such models to improve performance. Extensive experiments using five
real-world regression datasets that span computer vision, human-computer
interaction, and healthcare show that using SupCR achieves the state-of-the-art
performance and consistently improves prior regression baselines on all
datasets, tasks, and input modalities. SupCR also improves robustness to data
corruptions, resilience to reduced training data, performance on transfer
learning, and generalization to unseen targets.

本文提出了一种名为 Supervised Contrastive Regression（SupCR）的框架，该框架通过比较样本之间的目标距离来学习具有回归意识的表示，可以与现有回归模型组合以提高性能，在计算机视觉、人机交互和医疗保健等五个真实回归数据集上的实验证明，使用 SupCR 可以达到最先进的性能，同时在所有数据集、任务和输入模式上，始终提高了回归基线的性能。此外，SupCR 还可以改善对数据损坏的鲁棒性，对减少的训练数据的弹性，以及奇异目标的学习泛化能力。