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. Conversely, 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. Serving the similarities of the predictions as an indicator of feature similarities, ConR discerns the dissagreements between the label space and feature space and imposes a penalty on these disagreements. ConR minds 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 three large-scale deep imbalanced regression benchmarks. Our code is publicly available in https://github.com/BorealisAI/ConR.

翻译：不平衡分布在现实世界数据中普遍存在。这些分布对深度神经网络施加了约束，要求其能够表征少数标签并避免偏向多数标签。大量不平衡方法针对离散标签空间，但无法有效扩展到标签空间连续的回归问题。相反，连续标签之间的局部和全局关联为在特征空间中有效建模关系提供了宝贵见解。在本工作中，我们提出ConR——一种对比正则化器，它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坍缩至其多数近邻。ConR利用预测相似性作为特征相似性的指示器，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。ConR通过两种对比策略关注标签空间的连续性质：对错误邻近性的惩罚与标签相似性成比例，同时鼓励正确邻近性建模局部相似性。ConR将核心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，并平滑扩展至单维和多维标签空间。我们的综合实验表明，ConR显著提升了三个大规模深度不平衡回归基准上所有最先进方法的性能。我们的代码已在https://github.com/BorealisAI/ConR公开。