Data Augmentation Approach

Data augmentation techniques aim to enhance the size and diversity of training datasets, improving the performance and robustness of machine learning models across various domains. Current research focuses on developing sophisticated augmentation strategies tailored to specific tasks, such as generating synthetic data that addresses issues like intersectional fairness or compositional generalization, and employing generative models like GANs and Neural Radiance Fields to create realistic augmentations. These advancements are significant because they address data scarcity challenges in many fields, leading to more accurate and reliable models for applications ranging from natural language processing and computer vision to software engineering and VLSI circuit design.