  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.
