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Deep neural networks have gained tremendous importance in many computervision tasks. However, their power comes at the cost of large amounts ofannotated data required for supervised training. In this work we review andcompare different techniques available in the literature to improve trainingresults without acquiring additional annotated real-world data. This goal ismostly achieved by applying annotation-preserving transformations to existingdata or by synthetically creating more data.

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