论文标题
混合真实和合成数据以增强神经网络训练 - 对当前方法的审查
Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches
论文作者
论文摘要
深层神经网络在许多计算机视觉任务中都非常重要。但是，它们的权力是以监督培训所需的大量注释数据来实现的。在这项工作中，我们审查和比较文献中可用的不同技术，以改善培训结果，而无需获得其他注释的现实数据。该目标主要是通过将注释传播转换应用于现有数据或合成创建更多数据来实现的。
Deep neural networks have gained tremendous importance in many computer vision tasks. However, their power comes at the cost of large amounts of annotated data required for supervised training. In this work we review and compare different techniques available in the literature to improve training results without acquiring additional annotated real-world data. This goal is mostly achieved by applying annotation-preserving transformations to existing data or by synthetically creating more data.
