Semantic Data Augmentation

Semantic data augmentation enhances machine learning models by generating synthetic training data that preserves the meaning of existing data, addressing data scarcity and improving model robustness. Current research focuses on developing unsupervised and computationally efficient methods, employing techniques like Bayesian inference and knowledge graph integration to create semantically meaningful augmentations across various domains, including natural language processing and medical image analysis. These advancements are significant because they improve model performance, particularly in challenging scenarios like zero-shot learning and long-tailed classification, leading to more accurate and reliable applications in diverse fields.