Strong Data Augmentation

Strong data augmentation techniques are being extensively researched to improve the performance of deep learning models, particularly in low-resource scenarios where labeled data is scarce. Current efforts focus on developing sophisticated augmentation strategies, including generative adversarial networks (GANs), latent diffusion models, and novel approaches like "dominant shuffle" for time series data, often combined with consistency training or semi-supervised learning methods. These advancements are crucial for enhancing model robustness, generalizability, and efficiency across diverse applications, from natural language processing and medical image analysis to autonomous driving and personalized medicine.