作者: Shuochao YaoYiran ZhaoHuajie ShaoChao ZhangAston ZhangShaohan HuDongxin LiuShengzhong LiuLu SuTarek Abdelzaher
刊名: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, Vol.2 (3), pp.1-21
来源数据库: Association for Computing Machinery Journal
DOI: 10.1145/3264954
关键词: Deep LearningGANInternet-of-ThingsMobile ComputingSemi-Supervised Learning
原始语种摘要: Recent proliferation of Internet of Things (IoT) devices with enhanced computing and sensing capabilities has revolutionized our everyday life. The massive data from these ubiquitous devices motivate the creation of intelligent IoT systems that can collectively learn. However, labelling data for learning purposes is extremely time-consuming, which greatly hinders deployment. In this paper, we describe a semi-supervised deep learning framework, called SenseGAN, that can leverage abundant unlabelled sensing data thereby minimizing the need for labelling effort. SenseGAN jointly trains three components with an adversarial game: (i) a classifier for predicting labels of input sensing data; (ii) a generator for generating sensing data samples based on the input labels; and (iii) a...
全文获取路径: ACM  (合作)

  • learn 学习
  • classifier 分级机
  • discriminator 鉴别器
  • unlabelled 无标号
  • generator 振荡器发生器
  • sensing 感觉
  • consuming 耗的
  • demonstrate 说明
  • applications 应用程序
  • multimodal 多峰