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。