Large Scale Annotated

Large-scale annotated datasets are crucial for training effective machine learning models, particularly in domains like natural language processing and computer vision, but creating them is expensive and time-consuming. Current research focuses on mitigating this challenge through techniques like data augmentation using diffusion models, leveraging the transfer learning capabilities of large language models for zero-shot learning, and developing more robust loss functions to handle noisy data. These advancements are improving model performance and generalizability while reducing the reliance on massive manually annotated datasets, impacting various fields from medical diagnosis to retail optimization.