Augmented Sample

Augmented samples, synthetically generated training data, are increasingly used to improve the performance and robustness of machine learning models, particularly in scenarios with limited real-world data. Current research focuses on developing sophisticated augmentation strategies that address issues like bias mitigation, noise reduction, and efficient sample selection, often incorporating techniques like mixup, contrastive learning, and meta-learning within various model architectures. These advancements are significant because they enhance model generalization, calibration, and efficiency, leading to improved accuracy and reliability across diverse applications, including image classification, natural language processing, and reinforcement learning.