Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches

Viktor Seib; Benjamin Lange; Stefan Wirtz

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arXiv · 2007.08781

Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches

Viktor Seib·Benjamin Lange·Stefan Wirtz

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Abstract

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.

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Viktor Seib, Benjamin Lange, Stefan Wirtz. 2020-07-17. Mixing Real and Synthetic Data to Enhance Neural Network Training -- A Review of Current Approaches. https://arxiv.org/abs/2007.08781

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