Data Augmentation Technique

Data augmentation techniques artificially expand datasets by modifying existing samples, primarily aiming to improve the performance and robustness of machine learning models, especially when training data is scarce or imbalanced. Current research focuses on developing and evaluating novel augmentation methods tailored to specific data modalities (images, text, audio, time series) and model architectures (CNNs, Transformers, etc.), often incorporating automated machine learning for optimization. These techniques are proving valuable across diverse applications, from medical image analysis and natural language processing to robotics and internet traffic classification, enhancing model accuracy and generalization capabilities.