Imbalanced Regression
This software repository provides the official implementation for Deep Imbalanced Regression (DIR), a research framework presented at ICML 2021. It addresses the challenge of learning from imbalanced datasets where target values are continuous rather than categorical. DIR aims to handle natural imbalances, mitigate missing data in specific target regions, and ensure models generalize across the entire continuous range. The toolkit introduces two core techniques: Label Distribution Smoothing (LDS) and Feature Distribution Smoothing (FDS), which leverage smoothed label and feature distributions to improve model robustness. The package includes curated benchmarks for computer vision, natural language processing, and healthcare domains. Specific datasets covered include IMDB-WIKI-DIR and AgeDB-DIR for age estimation, NYUD2-DIR for depth prediction, STS-B-DIR for text similarity scoring, and SHHS-DIR for health condition assessment. The codebase is organized into dataset-specific subfolders that guide users throug