With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research.

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深度学习的移动众包技术，方法和挑战：一项调查

随着移动计算技术的日益普及以及外包战略在劳动密集型工业领域中的广泛应用，移动众包最近成为解决具有快速响应要求的复杂计算任务的有希望的解决方案。但是，移动众包任务的复杂性使得难以在有限的计算资源以及各种任务约束下追求最佳分辨率。在这种情况下，深度学习提供了通过训练一组最佳参数来追求这种最佳分辨率的有前途的方法。在过去的几十年中，许多研究人员致力于这个热门话题，并提出了各种最前沿的解决方案。鉴于此，我们从技术，方法和挑战的角度回顾了移动众包深度学习的研究现状。最后，我们列出了一组尚待解决的挑战，需要在未来的研究中进行深入研究。