Spectral Augmentation

Spectral augmentation is a data augmentation technique used to improve the performance of machine learning models, particularly in scenarios with limited labeled data or significant domain shifts. Current research focuses on applying spectral augmentation to various data types, including graphs, images, and speech signals, often within contrastive learning frameworks or generative adversarial networks (GANs). This approach enhances model robustness and generalization by creating diverse training samples that capture important spectral features, leading to improved performance in tasks like graph classification, hyperspectral target detection, and image segmentation in precision farming. The effectiveness of different spectral augmentation strategies, and their interplay with other augmentation methods, remains an active area of investigation.