Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches
This is an incremental review addressing the data scarcity problem in computer vision for researchers and practitioners.
The paper reviews techniques to enhance neural network training by using synthetic data or transformations to reduce the need for annotated real-world data, summarizing current approaches without presenting new experimental results.
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.