Feature Augmentation

Feature augmentation enhances machine learning models by enriching their input data or internal representations with additional information. Current research focuses on optimizing the selection and application of augmentation techniques, exploring various methods across different data types (images, text, tabular data, graphs) and model architectures (including neural networks, gradient boosting, and diffusion models). This approach improves model performance, particularly in scenarios with limited data, imbalanced classes, or domain shifts, leading to more robust and accurate predictions across diverse applications. The impact spans various fields, including image classification, medical diagnosis, and social network analysis.