Imbalanced distributions are ubiquitous in real-world data. They create constraints on Deep Neural Networks to represent the minority labels and avoid bias towards majority labels. The extensive body of imbalanced approaches address categorical label spaces but fail to effectively extend to regression problems where the label space is continuous. Local and global correlations among continuous labels provide valuable insights towards effectively modelling relationships in feature space. In this work, we propose ConR, a contrastive regularizer that models global and local label similarities in feature space and prevents the features of minority samples from being collapsed into their majority neighbours. ConR discerns the disagreements between the label space and feature space and imposes a penalty on these disagreements. ConR addresses the continuous nature of label space with two main strategies in a contrastive manner: incorrect proximities are penalized proportionate to the label similarities and the correct ones are encouraged to model local similarities. ConR consolidates essential considerations into a generic, easy-to-integrate, and efficient method that effectively addresses deep imbalanced regression. Moreover, ConR is orthogonal to existing approaches and smoothly extends to uni- and multi-dimensional label spaces. Our comprehensive experiments show that ConR significantly boosts the performance of all the state-of-the-art methods on four large-scale deep imbalanced regression benchmarks. Our code is publicly available in https://github.com/BorealisAI/ConR.

翻译：不平衡分布在真实世界数据中普遍存在。它们对深度神经网络施加约束，要求其能表征少数标签并避免对多数标签的偏向。现有大量处理不平衡问题的方法主要针对离散标签空间，但难以有效推广至标签空间连续的回归问题。连续标签间的局部与全局关联为特征空间中的有效建模提供了重要见解。本文提出ConR——一种基于对比学习的正则化器，它能在特征空间中建模全局与局部标签相似性，并防止少数样本的特征向其多数邻域塌缩。ConR能识别标签空间与特征空间之间的不一致性，并对这些不一致施加惩罚。该正则化器通过两种对比策略处理标签空间的连续特性：根据标签相似度按比例惩罚错误邻近关系，同时鼓励正确邻近关系建模局部相似性。ConR将关键考量整合为一种通用、易集成且高效的方法，有效解决了深度不平衡回归问题。此外，ConR与现有方法正交，可平滑扩展至单维及多维标签空间。全面实验表明，ConR在四个大规模深度不平衡回归基准上显著提升了所有最先进方法的性能。我们的代码已开源在https://github.com/BorealisAI/ConR。