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The use of simulated virtual environments to train deep convolutional neural networks (CNN) is a currently active practice to reduce the (real)data-hungriness of the deep CNN models, especially in application domains in which large scale real data and/or groundtruth acquisition is difficult or laborious.
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Xu, J., Ramos, S., Vázquez, D., and López, A. M. (2014) · 2014
Cited alongside, same era.
Model validation for vision systems via graphics simulation
Veeravasarapu, V., Hota, R. N., Rothkopf, C., and Visvanathan, R. (2015a)
Cited in the paper.
Simulations for validation of vision systems
Veeravasarapu, V., Hota, R. N., Rothkopf, C., and Visvanathan, R. (2015b)
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The cityscapes dataset for semantic urban scene understanding
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