It has been demonstrated that the amount of data is crucial in data-driven
machine learning methods. Data is always valuable, but in some tasks, it is
almost like gold. This occurs in engineering areas where data is scarce or very
expensive to obtain, such as predictive maintenance, where faults are rare. In
this context, a mechanism to generate synthetic da
这篇论文对合成数据增强技术进行了广泛评估,包括基于真实 3D 图形建模、神经风格迁移、差分神经渲染、生成对抗网络和变分自编码器等生成人工智能技术。对于每一种方法类别,我们关注重要的数据生成和增强技术、广泛的应用范围和具体的用例,以及现有限制和可能的解决方法。此外,我们总结了用于训练计算机视觉模型的常见合成数据集,强调主要特点、应用领域和支持的任务。最后,我们讨论了合成数据增强方法的有效性,并希望通过这篇详细的论文为读者提供必要的背景信息和深入了解现有方法及相关问题。