Sampling Based Data Augmentation

Sampling-based data augmentation enhances machine learning model training by generating synthetic data to address issues like limited datasets, noisy labels, and class imbalance. Current research focuses on developing adaptive sampling strategies that intelligently select augmentations, exploring the interplay between augmentation techniques and open-set recognition, and using augmentation to mitigate biases arising from noisy or imbalanced data. These advancements improve model robustness, accuracy, and generalization across various applications, including 3D object detection, graph matching, and image classification, ultimately leading to more reliable and efficient machine learning systems.