This repository contains the implementation code for paper: Delving into Deep Imbalanced Regression.
Deep Imbalanced Regression (DIR) aims to learn from imbalanced data with continuous targets,
tackle potential missing data for certain regions, and generalize to the entire target range.
Existing techniques for learning from imbalanced data focus on targets with categorical indices, i.e., the targets are different classes. However, many real-world tasks involve continuous and even infinite target values. We systematically investigate Deep Imbalanced Regression (DIR), which aims to learn continuous targets from natural imbalanced data, deal with potential missing data for certain target values, and generalize to the entire target range.
We curate and benchmark large-scale DIR datasets for common real-world tasks in computer vision, natural language processing, and healthcare domains, ranging from single-value prediction such as age, text similarity score, health condition score, to dense-value prediction such as depth.
- [02/18/2021] ArXiv version posted. The code is currently under cleaning. Please stay tuned for updates.
@article{yang2021delving,
title={Delving into Deep Imbalanced Regression},
author={Yang, Yuzhe and Zha, Kaiwen and Chen, Ying-Cong and Wang, Hao and Katabi, Dina},
journal={arXiv preprint arXiv:2102.09554},
year={2021}
}