Data Augmentation Method

Data augmentation methods aim to enhance the performance of machine learning models by artificially increasing the size and diversity of training datasets. Current research focuses on developing augmentation techniques tailored to specific data modalities (images, text, audio, tabular data) and tasks, often leveraging generative models like diffusion models and large language models to create more realistic and semantically diverse synthetic data. These advancements are significant because they address the limitations of limited or imbalanced datasets, improving model robustness, generalization, and ultimately, the accuracy and reliability of AI systems across various applications.