State of the Art Data

State-of-the-art data augmentation techniques aim to improve the performance and robustness of machine learning models, particularly when training data is limited or imbalanced. Current research focuses on developing sophisticated augmentation strategies using diffusion models and other generative methods, often incorporating human-in-the-loop approaches for better control and interpretability, as well as leveraging graph representations for structured data like RTL designs. These advancements are crucial for various applications, including defect detection, medical image analysis, and natural language processing, enabling more accurate and reliable models even with scarce or noisy data.