Semantic Augmentation

Semantic augmentation enhances machine learning models by enriching training data with semantically similar, yet varied, examples. Current research focuses on generating these augmentations using various methods, including leveraging pre-trained image-text models, adversarial techniques in feature space, and incorporating structured semantic representations like AMR graphs. This approach improves model generalization, particularly in data-scarce domains like robotics, medical image analysis, and natural language processing, leading to more robust and efficient models across diverse applications.