Virtual Dataset

Virtual datasets, synthetically generated datasets mirroring real-world data characteristics, are increasingly used to address limitations in acquiring and annotating real data for training machine learning models. Current research focuses on adapting models trained on real data to virtual counterparts, leveraging deep neural networks (DNNs) for tasks like image segmentation, object detection, and radar imaging, often incorporating domain adaptation techniques to bridge the gap between simulated and real environments. This approach is particularly valuable in scenarios with limited real data due to cost, privacy concerns, or data scarcity, enabling the development of robust and accurate AI models across diverse applications, including robotics, medical imaging, and educational technology.