4 papers
More Data or Better Algorithms: Latent Diffusion Augmentation for Deep Imbalanced Regression
Shayan Alahyari
In many real-world regression tasks, the data distribution is heavily skewed, and models learn predominantly from abundant majority samples while failing to predict minority labels…
SMOGAN: Synthetic Minority Oversampling with GAN Refinement for Imbalanced Regression
Shayan Alahyari, Mike Domaratzki
Imbalanced regression refers to prediction tasks where the target variable is skewed. This skewness hinders machine learning models, especially neural networks, which concentrate o…
Regression Augmentation With Data-Driven Segmentation
Shayan Alahyari, Shiva Mehdipour Ghobadlou, Mike Domaratzki
Imbalanced regression arises when the target distribution is skewed, causing models to focus on dense regions and struggle with underrepresented (minority) samples. Despite its rel…
Local distribution-based adaptive oversampling for imbalanced regression
Shayan Alahyari, Mike Domaratzki
Imbalanced regression occurs when continuous target variables have skewed distributions, creating sparse regions that are difficult for machine learning models to predict accuratel…