Data Augmentation

Data augmentation is a technique used to artificially expand datasets by creating modified versions of existing data, primarily to improve the performance and robustness of machine learning models, especially when training data is scarce. Current research focuses on developing more sophisticated augmentation methods, including those leveraging generative models like GANs and diffusion models, and integrating augmentation with other techniques such as contrastive learning and transfer learning, often applied within architectures like transformers and convolutional neural networks. This work is significant because it addresses the limitations of limited datasets across various domains, from image classification and object detection to natural language processing and time series forecasting, leading to more accurate and generalizable models for diverse applications.