Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches
Motec GmbH（Motec有限公司）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
