  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
https://github.com/YyzHarry/imbalanced-regression.
