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
this https URL

提出了 ConR，一种对深度不平衡回归问题进行有效处理的方法，它通过在特征空间中模拟全局和局部标签相似性，并且通过先进的方法显著提升了现有方法在三个大规模深度不平衡回归基准上的性能。