Random Data Augmentation

Random data augmentation (RDA) enhances machine learning model training by introducing random transformations to input data, improving generalization and robustness. Current research focuses on optimizing RDA strategies for various data types (images, ECG signals, graphs) and model architectures (CNNs, contrastive learning models), often comparing RDA's effectiveness against more sophisticated, deterministic augmentation methods. This work is significant because effective RDA techniques can improve model performance, particularly in data-scarce scenarios or when dealing with noisy or complex data, leading to more reliable and accurate applications in diverse fields like medical image analysis and anomaly detection.