Learning from Simulated and Unsupervised Images through Adversarial Training


Roughly a month after Apple's announcement that it would be publishing its AI research, their first deep learning paper has been published. When learning from synthetic images performances may be impeded by the gap between real and synthetic images. To overcome this gap the authors present a method based on Simulated+Unsupervised (S+U) learning, where the task is to learn a model to improve the realism of a simulator's output using unlabeled real data, while preserving the annotation information from the simulator.


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