SaliencyGAN: Deep Learning Semisupervised Salient Object Detection in the Fog of IoT

Chengjia Wang, Shizhou Dong, Xiaofeng Zhao, Giorgos Papanastasiou, Heye Zhang*, Guang Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

89 Citations (Scopus)


In modern Internet of Things (IoT), visual analysis and predictions are often performed by deep learning models. Salient object detection (SOD) is a fundamental preprocessing for these applications. Executing SOD on the fog devices is a challenging task due to the diversity of data and fog devices. To adopt convolutional neural networks (CNN) on fog-cloud infrastructures for SOD-based applications, we introduce a semisupervised adversarial learning method in this article. The proposed model, named as SaliencyGAN, is empowered by a novel concatenated generative adversarial network (GAN) framework with partially shared parameters. The backbone CNN can be chosen flexibly based on the specific devices and applications. In the meanwhile, our method uses both the labeled and unlabeled data from different problem domains for training. Using multiple popular benchmark datasets, we compared state-of-the-art baseline methods to our SaliencyGAN obtained with 10-100% labeled training data. SaliencyGAN gained performance comparable to the supervised baselines when the percentage of labeled data reached 30%, and outperformed the weakly supervised and unsupervised baselines. Furthermore, our ablation study shows that SaliencyGAN were more robust to the common 'mode missing' (or 'mode collapse') issue compared to the selected popular GAN models. The visualized ablation results have proved that SaliencyGAN learned a better estimation of data distributions. To the best of our knowledge, this is the first IoT-oriented semisupervised SOD method.

Original languageEnglish
Pages (from-to)2667-2676
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Issue number4
Early online date4 Oct 2019
Publication statusPublished - Apr 2020


  • Convolutional neural networks (CNNs)
  • deep learning
  • generative adversarial network (GAN)
  • Internet of Things (IoT)
  • salient object detection (SOD)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering


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