Cut and Paste

Cut-and-paste data augmentation is a technique used to enhance the performance of machine learning models, particularly in image segmentation and other computer vision tasks, by artificially increasing the diversity of training data. Current research focuses on adapting this technique for various applications, including satellite imagery analysis, unsupervised domain adaptation, and mitigating the effects of poisoned datasets, often employing models like U-Net and leveraging techniques such as self-supervised learning and diffusion models. These advancements improve model generalization and robustness, leading to more accurate and reliable results in diverse fields ranging from environmental monitoring to medical image analysis and robotic control.