Data augmentation encompasses a diverse array of methods designed to expand and diversify training datasets without the need for additional manual annotation. In computer vision, common approaches ...

Data augmentation techniques rely on existing data sets to simulate events and forecast outcomes. Synthetic data provides larger, more diverse data sets and stand-in for data that's difficult to ...

An example of visual data augmentation techniques used in machine learning, which captures the main principle of variability effects: exposure to variation along non-discriminative dimensions (i.e., ...