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。

ConR：用于深度不平衡回归的对比正则化器

ConR: Contrastive Regularizer for Deep Imbalanced Regression

Real-world data often exhibit imbalanced distributions, where certain target
values have significantly fewer observations. Existing techniques for dealing
with imbalanced data focus on targets with categorical indices, i.e., different
classes. However, many tasks involve continuous targets, where hard boundaries
between classes do not exist. We define Deep Imbalanced Regression (DIR) as
learning from such imbalanced data with continuous targets, dealing with
potential missing data for certain target values, and generalizing to the
entire target range. Motivated by the intrinsic difference between categorical
and continuous label space, we propose distribution smoothing for both labels
and features, which explicitly acknowledges the effects of nearby targets, and
calibrates both label and learned feature distributions. We curate and
benchmark large-scale DIR datasets from common real-world tasks in computer
vision, natural language processing, and healthcare domains. Extensive
experiments verify the superior performance of our strategies. Our work fills
the gap in benchmarks and techniques for practical imbalanced regression
problems. Code and data are available at
this https URL

文章提出了一种新的方法 Deep Imbalanced Regression（DIR）来处理实际中连续性 target 的不均衡数据分布问题，通过分布平滑技术，使用已有数据来校准标签和学习特征的分布，同时构建了大规模的实际任务实验数据集，并在计算机视觉、自然语言处理、医疗保健等领域获得了良好的实验效果。